Multi-BK-Net: Multi-Branch Multi-Kernel Convolutional Neural Networks for Clinical EEG Analysis
Authors: Ann-Kathrin Kiessner
Tonio Ball
Joschka Boedecker
Venue: Accepted by TMLR ()

Abstract: Classifying an electroencephalography (EEG) recording as pathological or non-pathological is an important first step in diagnosing and managing neurological diseases and disorders. As manual EEG classification is costly, time-consuming and requires highly trained experts, deep learning methods for automated classification of general EEG pathology offer a promising option to assist clinicians in screening EEGs. Convolutional neural networks (CNNs) are well-suited for classifying pathological EEG signals due to their ability to perform end-to-end learning. In practice, however, current CNN solutions suffer from limited classification performance due to I) a single-scale network design that cannot fully capture the high intra- and inter-subject variability of the EEG signal, the diversity of the data, and the heterogeneity of pathological EEG patterns and II) the small size and limited diversity of the dataset commonly used to train and evaluate the networks. These challenges result in a low sensitivity score and a performance drop on more diverse patient populations, further hindering their reliability for real-world applications.
Here, we propose a novel multi-branch, multi-scale CNN called Multi-BK-Net (Multi-Branch Multi-Kernel Network), comprising five parallel branches that incorporate temporal convolution, spatial convolution, and pooling layers, with temporal kernel sizes defined by five clinically relevant frequency bands in its first block. 
Evaluation is based on two public datasets with predefined test sets: the Temple University Hospital (TUH) Abnormal EEG Corpus and the TUH Abnormal Expansion Balanced EEG Corpus. 
Our Multi-BK-Net outperforms five baseline architectures and state-of-the-art end-to-end approaches in terms of accuracy and sensitivity on these datasets, setting a new benchmark. Furthermore, ablation experiments highlight the importance of the multi-branch, multi-scale input block of the Multi-BK-Net. Overall, our findings indicate the efficacy of multi-branch, multi-scale CNNs in accurately and reliably classifying EEG pathology, demonstrating advantages in handling data heterogeneity compared to other deep learning approaches.  Thus, this study contributes to the ongoing development of deep end-to-end methods for general EEG pathology classification.

Published in Transactions on Machine Learning Research (12/2025)
Multi-BK-Net: Multi-Branch Multi-Kernel Convolutional Neural Networks for Clinical EEG Analysis
Ann-Kathrin Kiessner
Deportment of Computer Science & (BBTT//Bminnlinks-Baaa University of Freiburg
Joschka Boedecker
jboedeckåcs. jhoedeckkess..iifrrribbrrgg de
Department of Computer Science &
Tools, University of Freiburg & Collaboratioe Research
Institute Intelligend Oncology (CRIION)
Tonio Ball
tonio. tonio.ball@ www.llanskiiiikfrrreeeeeeeeeeeee freibkurrribbrrg.de de University of Preiburg
Neuromedical Al Lab, Medical Centre Preiburg & LA4BIT//Bnnnnnnnss-Bhhnlolo
Reviewed on OpenReview: https: // openreview. net/ forum? id= IsC10nZAd
Abstract
Classifying an electrenceepallogappy (EEG) recording as pathological or non- non-pathological important first step in diagnosing and managing neurological discases and disorders. As manual EEG classification is costly, time-consuming and requires highly trained ex- perts, deep learning methods for automated classification of general EEG pathology offer a promising option to assist clinicians in screening EFGs. Convolutional neural networks (CNNs) are well-suited for classifying pathological EEG signals due to thoir ability to per- form end-to-end learning. In practice, however, current CNN solutions sulfer from limited classification performance due 1) a single-scale network design that cannot fully cap- ture the high intra- and inter-subject variability of the EEG signal, the diversity of the data, and the heterogeneity of pathological EEG patterns and II) the small size and lim- ited diversity of the dataset commonly used to train and evaluate the networks. These challenges result in a low sensitivity score and a performance drop on more diverse patient populations, further hindering their reliability for real-world applications. Here, we propose a novel multi-branch, multi-scale CNN called Multi-BK-Nel. (Multi-Branch Multi-Kernel Network), comprising five parallel branches that incorporate temporal convolution, spatial convolution, and pooling layers, with temporal kernel sizes defined by five clinically rele- vant frequency bands in its first block. Evaluation is based two public datasets with predefined test sets: the Temple University Hospital (TUH) Abnormal EEG Corpus and the TUH Abnormal Expansion Balanced EEG Corpus. Our Multi-BK-NNet outperforms five baseline architectures and state-of-the-art end-to-end approaches in terms of accuracy and sensitivity on these datasets, setting a new benchmark. Furthermore, ablation experiments highlight the importance of the multi-branch, multi-scale input block of the Multi-BK-Net. Overall, our findings indicate the elficacy of multi-branch, multi-scale CNNs in accurately and reliably classifying EEG pathology, demonstrating advantages in handling data hetero- geneity compared to other deep learning approaches. Thus, this study contributes to the ongoing development of deep end-to-end methods for general EEG pathology classification.

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Published in Transactions on Machine Learning Research (12/2025)
1 Introduction
Electoelncehhalograalyy (EEG) is non-invasive method of measuring and recording electrical activity in the brain. EEG has high temporal resolution, low equipment cost, and high sensitivity to dynamic changes in neural signals. Due to its high efficiency and usability, EEG is most commonly used in clinical practice for the diagnosis and management of various neurological conditions (Lopez et al., 2015: Zhang et al.) 2023b). For this purpose, a preliminary important step in clinical practice is to classify an EEG recording as non- pathological or pathological (Brogger et al., [018 Lopez et al., 2015). To this end, human EEG experts visually analyse recordings from long-term monitoring multiple short sessions, which is tedious and time-consuming process (Brogger et al. 2018). They also consider various additional patient-related factors, such as medical history, age, or medication (Beuchat et al. 2021 Nayak & Anilkumar] 2020). Furthermore, it requires years of training to achieve a thorough understanding of pathological EEG patterns and to distinguish them from normal EEG, normal benign variants, and artefacts (Amin et al.) 2023 Emmady & Anilkumar] 2023 Hoppe 2018). Thus, these challenges result in inter-rater variability and diagnostic errors. For classifying EEG recordings as pathological or non-pathological, previous rescarch has reported
inter-rater agreement of 86 88% between two neurologists (Houfek & Ellingson 1959 Rose el al.) 1973). At
the same time, [Buchhtt et al. (2021) found even lower inter-rater agreement, 82-86% among multiple EEG technologists and neurologists.
In this sense, introducing automated EEG classification, or at least some level of automation such as clinical decision support systems, has the potential to improve or accelerate the EEG classification process, thereby enhancing the quality of patient care. To this end, deep lcarning approaches for EEG classification have
received increasing attention in recent years (for a detailed review, see (eeeann et al.) 2021 Craik et al. 2019
Faust et al. 2018: Rahman et al. 2024; Roy et al., 2019b Praveena et al.] (022). Tt has also spurred their application to the task of classifying general EEG pathology (Schirrmeister et al. 2017b). In this regard, rescarch has shown that convolutional neural networks (CNNs) have been quite effective for general EEG pathology classification due to their ability to extract relevant feature representations directly from raw or
minimally preprocessed EEG data (Darvishi-Baree Bayazi et al. 2023 Gemein et al.. 2020 Khan et al.) 2022
Kiessner et al.) 2023 2024; Van Leeuwen et al., 2019: Western et al. 2021 Appendix A.I provides more details on the related works.). In practice, however, the classification performance of current state-of-the art CNN methods is primarily limited by three factors. First,, the EEG presents inherent challenges which make the application of deep learning methods to real-world EEG datasets more difficult. For example, the recorded EEG signals are high-dimensional, non-linear, nonstationary (Cole & Voylek 2019 Gramfort lt al. 2013 Jia et al. 2021), have low signal-to-noise ratio Shigdely-Shamll et al. 2015 Jas et al. 2017). are strongly influenced by artefacts cansed by external environmental factors (c.g. eloctrical interference
from external sources) IIlam et al. 2016 Kane et al.] 2017) or physiological sources (e.g. cardiac, muscle activity, eye movements, or fatigue) (Britton et al. 20166), and variations in recording protocols and labelling standards within clinical data can occur (Poziomska et al. 2025). In addition, pathological EEG patterns exhibit wide range of physiological variability across both patients (Nahmias et al. 2019) and neurological
conditions (Fmmmay & Anilkumar 2023 Nayak & Anilkumar 2020 Smith 2005).
Due to these challenger
the classification performance can vary between patients (Altuwaijri et al. 2022
Lashgari et al. 2020 Roy
et al. 2019b Schirrmeister et al.] 2017b). Second, due to the scarcity of publicly available datasets, current
approachess are mainly trained and evaluated on a single dataset, the TUH Abnormal EEG Corpus (TUAB) [Tópez de Diego 2017). While it is therefore well established that these methods perform well on this small, homogeneous dataset, this limitation restricts the classification performance of these approaches. For example, recent studies have observed significant differences in the performance of several previously
established models after training or validating them other datasets (Darvishi- Bayazi et al. 2023 Khan
ct al.
2022
Kiessner et al. 2023 2024 Nahmias & Kontson 2020 Poziomska et al.] 2025 Van Leeuwen
el al.
2019
Western el al. 2021]. Lastly, single-scale convolutions, with manually and arbitrarily defined
kernel sizes
Emsawas et al. 2022). are primarily used to design CNNs; however, they are less capable of
handling individual differences in the EEG signal. For example, several studies have found that, due to
variability in the EEG signal, the optimal kernel size differs from subject to subject and from time to time
for the same subject (Altaheri et al. 2023b Altuwaijri et al.] 2022: Jía et al. 2021) While multi-branch,
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multi-scald or parallel architectures (Altuwaijri et al. 2020
2022 Belwafi et al.
2017 Ingolfsson et al 2020;
Jia et al. 2021 Riyad et al.
Zhang et al. 2023a have shown promising results in addressing the challenge of heterogeneity in various EEG classification tasks (Cai et al. 2024 Jia et al.
Szegedy et al. 2015
2025 Zhu et al. 2023). only a few attempts have been made to address the challenges of general EEG pathology classilication by using CNNs with a set of three convolution scales 2019a; Wu et al.) 2021 However, these CNN approaches have achieved classification performance similar to that of other CNNs, with accuracies ranging from 85.10% to 87.10%; they also inherit the disadvantages found in their single-scale counterparts. For example, these results were based on the TUAB, and no attempts have been made to evaluate the performance of these CNNs on larger, more heterogeneous dataset. In addition, smaller kernel sizes are preferred due to lower computational costs Emsawws el al.] 2022), which, however, tend to learn shorter temporal patterns from faster frequency bands Coheen 2014 Jia et al. 2021). This also restricts the ability of these CNNs to capture the heterogeneity of the EEG signal (Altaheri ct al., 2023b Emsawas et al. 2022). Therefore, developing an accurate and reliable CNN solution for general EEG pathology classilication requires a network design that addresses the high intra- and inter-subject variability in the EEG signal, data diversity, and the heterogeneity of pathological EEG patterns.
2021 Siddiga et al.)
2024 Yan et al.
BBrenner et al. 2024 Roy ct al.,
In this work, we propose a novel, multi-brance multi-scale CNN, called (Multi-BK-Net) for general EEG pathology classification that extracts long-term and short-term spatiotem- poral EEG features by incorporating five parallel branches within the first convolution
Multi- Kernel Network
block (see Section 2.1 Each branch employs a temporal convolution layer with a different kernel size that was defined considering five clinically relevant frequency bands, namely delta. (1-3 Hz), theta (4-7 (4))Hz), alpha (8-13 Hz), beta (14-30 Hz) and low gamma (30-80 Hz) (Brenner et al. 2024). Aiming at the problem of small amount of training data and to increase data diversity, we combined the predefined training sets of two publicly avail- able datasets for general EEG pathology classification, the TUH Abnormal EEG Corpus (TUAB) (López de Diego 2017] and TUH Abnormal Expansion Balanced EEG Corpus (TUABEXB) (Kiessner et al., 2023). to optimise and train our Multi-BK-Net. The hyperparameters of the Multi-BK-Net were optimised using multivariate tree-structured Parzen estimators (TPE) (Bergstra et al., 2011 2013) from the Optuna library (Akiba et a 2019) with respect to the mean validation accuracy and mean validation sensitivity values from a 5-fold cross-vale vross-vllidttion. Our study presents the following contributions to rescarch on deep end-to-end CNNs for general EEG pathology classification:
We propose the Multi-BK-Net,, a multi-branch, multi-scale CNN designed for general EEG pathology classification. We combine the concepts of multi-scale and multi-branch CNNs with the idea of adapting the temporal kernel size based on five clinically relevant frequency bands to address the challenge of heterogeneity inherent in the EEG signal.
We evaluate our proposed Multi-BK-Net on the predefined test sets of two publicly available datascts, the TUAB and the TUABEXB datasets. Our Multi-BK-Net achieves mean accuracies of 87.75% and 87.01% and mean sensitivities of 83. 10% and 84.25% respectively, and thus outperforms five architectures used as a baseline (Section 3.4), as well as previously reported state-of-the-art deep learning approaches (Section 3.5p on these datasets, setting a new benchmark. In addition, with mean accuracies in the narrow range of 87-88% the Multi-BK-Net approaches the upper limit of inter-rater agreement among human experts, thus bringing deep learning- learning-based general EEG pathology classification closer to human-level performance. An improvement in evaluation sensitivity demonstrates further practical gains of our method. In comparison to the baseline architectures, Multi-BK-Net achieved the highost mean sensitivitics at a fixed specificity of 95% on both test sets (82.78% and 84.25%). The significance of this result lies in applications such as deep learning- based EEG classification and clinical decision support systems in clinical practice, for which a high, robust sensitivity is crucial for accurately identifying EEG pathology. In deep learning-based clinical EEG analysis, high accuracy and sensitivity are crucial for improved patient outcomes, reduced diagnostic errors, and enhanced clinical efficiency. Thus, even minor gains significantly improve the reliability and trustworthiness of such systems.
In this work, we will use "multi-scale" and 'multi-kernel' interchangeably.
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Our ablation experiments show that using both multiple temporal kernel lengths and multiple par- allel branches in the first. block significantly improves the classification performance of the model, particularly in terms of mean accuracy and mean sensitivity (Section 3.6).
To interpret the trained Multi-BK-Ner we visualise the learned features using Uniform Manifold Ap- proximation and Projection (UMAP) (Melnnes et al., 2018) method (Section 3.7) and observe that the Multi-BB-Ne forms distinct and compact clusters of samples from the pathological and non- pathological classes, which partly explains the good performance of this architecture. Additionally, we perform an amplitude gradient analysis (Section 3.8) that shows that the model's pathological prediction is sensitive to localised patterns of amplitude changes across different frequency bands. These patterns align with the current neurophysiological knowledge for pathological EEG patterns (Amin ct al. 2023 Emmady & Anilkumar 2023; Hoppe
2018 Kane et al.) 2017 Medithe & Nelaku- diti 2016; Tatum & William 2202]) and with the pathological patterns that have been identified by human experts in the corresponding clinical EEG reports.
A qualitative review of clinical EEG reports corresponding to false negative and false positive clas- sified EEG recordings by the Multi-BK-Net suggests that the CNN struggles to identify context- dependent abnormalitics. While clinicians incorporate additional information about the patient's age, vigilance, medications, and clinical history to interpret. EEG patterns, our CNN, which was trained only on the EEG signal, lacks access to this contextual information. This results in the model being at disadvantage relative to clinicians and highlights the potential for further perfor- mance gains by incorporating contextual information into the training and classification processes.
Overall, our results demonstrate the efficacy of our multi-branchh, multi-scale CNN solution in ac- curately and reliably classifying general FFG pathology. Our findings contribute to the broader discussion on the challenges of deep learning- based general EEG pathology classification, providing new insights for the design of future end-to-end CNNs. This will ultimately advance the development of more reliable and robust methods for deep learning- based general EEG pathology classification.
2 Methods
2.1
Multi-Kernel Network (Multi-BK-Net)
We propose a novel deep, multi-scale, and multi-branch CNN, called Multi-BK-Net, for general EEG pathol- ogy classification. This model extracts long-term and short-term spatiotemporal EEG features by incor- porating a dedicated multi-branch multi-scale first block that processes EEG inputs for different temporal patterns. The block is followed by three standard norvolution-mcan-pollne blocks and a softmax classi- fication layer. Figure [ shows the overall structure of the proposed Multi-BK-Net architecture. The first convolutional block is divided into five branches (multi-branches). Each convolutional branch contains two convolutional layers and a mean-pooling layer. The first convolutional layer performs temporal filtering with set of seven convolutional filters. Each branch uses different kernel size for temporal filtering (multi- scale). This allows the model to extract and combine features from different time scales. The choice of kernel size was based on five frequency bands, namely, delta (0.5 to 4 Hz), theta (4 to 7 Hz), alpha (8 to 12 Hz), beta (13 to 30 Hz), and low gamma (30 to 80 Hz) (Brenner et al. 2024 Nayak & Anilkumar 2020). by also considering the sampling rate. In this study, the EEG signal is represented as a 2D input, with the dimension (ExT) and with a window size of 60 seconds and a sampling frequency of 100 Hz, corresponding to 6000 time points, an input shape of 21 x 6000 (for more details, see Section 3.1). Accordingly, we used five different temporal kernel sizes: 1 1x200, 1 1x25, 1x13, 1x7 and 1x3. In the second layer, spatial con- volution is applied. Each filter performs a spatial convolution with weights for all possible electrode pairs, using the filters from the preceding temporal convolution. Note that there is no activation function between the two layers; therefore, in principle, they could be combined into a single layer. However, we chose a split convolution because it has been shown to outperform a combined temporal-spatial convolutional layer (Schirrmeister et al.) 2017a b). After the spatial convolution, mcan pooling is applicd. As successfully done by [Schirrmeister et al. 2017a), the pooling strides were moved directly to the convolutional layers preceding each pooling. Finally, the outputs of each branch are concatenated along the feature axis, resulting in
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Table 1: Hyperparameters of the Multi-BK-Net architecture. Hyperparameters have been optimised preliminary experiments; more details on the hyperparameter optimisation and design choices are provided in Appendix A.3
Hyperparameter
Selected value
Total number of temporal convolution filters Normalisation
35 filters per branch)
GroupNorm
Activation functions
GELU
Pooling mode first block
Mean
Pooling mode remaining blocks
Mean
Forth conv-pooling-bock
True
Forth conv-pooling-blece broader
True
Pool length
Pool stride
Stride before pool
Truc
Dropout Filter length convolution blocks
0.502959339666169
20
Input window size
6000
Weighted loss factor pathological
Optimizer
AdamW
Optimizer betal
0.5
Learning rate
0.00314143664096615
Weight decay
1.83974005895331244--
Batch size
64
Number of epochs
42
Number of channels
21
leature vector shape (1, 35, 397, 1). The concatenated lensors are then passed through three convolution- mean-pooling blocks. Each pooouutioo-mma-poollngg block consists of a convolutional layer and mean pooling layer. The output of the fourth block is then passed through softmax classification layer to receive the final prediction. In total, the Multi-BK-NG contains 1,038,683 trainable parameters. We implemented our model in Braindecode (BD), an open-source Python toolbox for decoding raw electrophysiologiaal brain data with deep learning models (Schirrmeister et al. 2017b). The Xavier initialisation method (Glorot & Bengio 2010) was used to initialise the weights. The biases have been initialised to 0. We applied group normalisatinn (Wu He 2018) to the output the convolutional layers before the nonlinearity. We used Gaussian Error Linear Units, or GELUs (GELU(() = x xP(( S x) = x+())) (Hendrycks Gimpel 2023
as activation functions. Additionally, we used dropout as a regularisation strategy (Srivastava al. 2014).
Appendix A.3 provides more detailed description of the design choices and hyperparameter optimisation procedure.
2.2 Network Training
For optimisation, used the adaptive moment estimation with decoupled weight decay (AdamW) optimiser [oshchilov & Hutter, 2019) with an optimised learning rate and betal parameter, and the negative log- likelihood loss (Terven et al. 2024). We used cosine anncaling (Loshchilov & Hutter 2016) to schedule the learning rates for both the gradient and weight decay updates, and refrained from restarting the learning rate. We trained the model on non-overlapping, equally sized time windows size 60 seconds using the trial-wise training strategy as described by Schirrmeister et al. (2017b). We repeated the training 10 times, using a fixed set of random seeds to account for model variance arising from random weight initialisation. The final predictions obtained by first. averaging the predictions of each window for each recording,
During trial-wise training, complete window pushed through the network. The network then produces prediction, which compared the target (lahel) for that window (trial) to compute the loss.
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Convelution
Convelution
Uniss
Hean Pooting
Stride 15x1
Concatenate
Cate-Pool Bloce
Convotution
70 Units 3x3
Mean Pooling
Strice 3x2
70
Cone-Pool Hoce
Convolution
100
2x2
10
Care-Posi Bloce
Convotution
200
Mean Pooling
Stiie 1x]
250
Roation
Convelution Softmax
280
Pathological
Normal
Figure 1: Multi-BK-Ne architecture. The first block is divided into five branches, each applying temporal convolution with different kernel size, followed by spatial convolution. There is no activation function between the two convolutional layers; after that, a mean-pooling layer is added. The EEG input (at the top) is passed through each branch. The outputs of all branches are concatenated and then used as input for the subsequent convoltion-mean-pooliin blocks. Black cuboids: input /feat/fe maps; coloured cuboids: convo- kernels. The corresponding sizes are shown black or in colour. Note that the proportions of maps and kernels in this schematic are only approximate.
and then calculating the overall performance across recordings (sce Scction 3.3 for more details). The list of hyperparameters for training is provided in Table [ The code of this study is available at https: //ggthab..co//mgg////utttttt Net- -ateregeeeelll-pppoooooorrrreeee

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3 Experiments and Results
To demonstrate the efficacy of our method, we evaluated the Multi-BK-Net on two public datasets for gen- cral EEG pathology classification: I) the small, homogencous, public dataset TUH Abnormal EEG Corpus (TUAB) (López de Diego, (017)) for comparison with previously published accuracies and IT) the larger, recently introduced TUH Abnormal Expansion Balanced EEG Corpus (TUABEXB) (Kiessner al., (023) to evaluate the classification methods with larger heterogencous number of EEG recordings. First, we compared the performance of the Multi-BK-Net to five baseline architectures. Following this, we conducted a performance comparison with previously reported state-of-the-art deep learning approaches. We then performed ablation experiments to demonstrate the superiority of the proposed Multi-BK-Net, further high- lighting the importance of using multiple temporal kernel sizes and multiple branches in the first block. In addition, we visualised the learned features of the Multi-BK-Net to highlight its robust and provide a comprehensive understanding of model performance. Lastly, we performed an amplitude gradicnt study to determine how sensitive the Multi-BK-Nel's predictions are to amplitude changes across different frequency bands.
3.1 Datasets and Preprocessing Steps
The TUAB consists of 2,993 recordings (49.18% pathological) from 2,329 patients (52.09% female, mean age: 48.55 + 17.86 years) that are divided into a predefined training set (2,717 recordings) and an evaluation set (276 recordings). In contrast, the TUABEXB contains 8,879 recordings (49.75% pathological) obtained from 7,006 patients (mean age: 47.7 = 21.2 years; 51.7% female) and is divided into a predefined training set (7990 recordings) and an evaluation set (889 recordings). For training, we concatenated the TUAB and TUABEXB training sets, which we refer to as the TUH Abnormal Combined EEG Corpus (TUABCOMB).
The TUABCOMB contains 10,707 recordings from 8,549 patients (mean age: 47.91 + 20.53 years; 52.5% female), of which 5,321 recordings (49. (997 are classified as pathological. We evaluated our models the predefined test sets of the TUAB and TUABEXB datasets, respectively (for more details on the datasets, see Section 3.3 There is no overlap of patients between the TUAB and the TUABEXB datasets. In both datasets, there is no overlap between patients in the predefined training set and the final evaluation set.
We applied the following minimal preprocessing steps to the raw EEG data: a) selected a set of 21 electrode positions f present in all recordings: b) discarded the first 60 seconds of each recording as they contain stronger artefacts; c) used up to 20 minutes of the remaining recording to speed up the computations: d) clipped the EEG recordings at + 800 μV to reduce the effect of strong artefacts; e) re-referenced all recordings to the Common Average Reference (CAR) f) resampled the data to 100 Hz to account for the different sampling rates and to further speed up the computation. To apply CNNs to EEG classification, the raw EEG signals measurod in microvolts (μVV can be represented as input as a 2D array with the number of time steps (T, temporal dimension) as the width and the number of rodc channels (E, spatial dimension) as the height, resulting in an input shape of EXT. To create the inputs for the networks, we process sliding windows over the EEG data D E T.. where T denotes the recording duration and E the number of electrodes. Windows are created input features 2, E R with input dimensionality I E [T/S] fa time point 4, where S is the stride and fs is the sampling frequency. We use non-overlapping windows of 60 seconds, which corresponds to stride of S = 60 seconds. The corresponding labels are y E 10 where y = 0 indicates a non-pathological window and y; = 1 indicates a pathological window. A tensor of shape (batch_sizee E, T) is provided as input to the architectures.
3.2 Baselines
To illustrate the superiority of the proposed Multi-BK-Nel, we compare it to several approaches reported in previous work. For general EEG pathology classification, comparing approaches is challenging without evaluating them within the same framework, because factors such as different evaluation methods, prepro- cessing steps, training strategies, and datasets or dataset versions can introduce bias. Therefore, to evaluate the performance of our architecture, we compared our method with the following five baseline architectures:
3-his set of EEG channels included the channels A1, A2, C3, C4, Oz, F3, F4, F7, F8, Fpl, Fp2, Fz, 01, O2, P3, P4, Pz, T3, T4, T5 and T6.
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Deep4Net (Schirrmeister et al.
2017b CNN consisting of four cuvohution-max-poooee blocks.
ShallowNet (Schirrmesste et al.
2017b ay, a CNN with a convolution pooolliniiiooooiingggogooooiniio block that was inspired
by the Filter Bank Common Spatial Patterns pipeline (Ang et al. 2008
Chin et al. 2009).
TCN [Bai et al. 2018 residual blocks.
Chrabaszcz
2018
Gemein et al. 2020 a temporal CNN consisting of five
EEGNet Lawhern et al.
2018
compact CNN with depthwise and separable convolutions.
ChronoNet (Roy et al. 2019a a deep recurrent neural network combining multiple ConviD layers with multiple filters of varying sizes and stacked Gated Recurrent Unit (GRU) layers.
These architectures have been implemented in Braindecode and have also demonstrated high performance on TUAB and/or TUABEXB across various studics using different preprocessing steps. Additional dctails on the baseline architectures, the list of hyperparameters for training, and the training procedure are provided in the Appendix A..4
3.3 Evaluation of Classification Performance
We trained each architecture on the full TUABCOMB training set and then evaluated the models on the withheld final evaluation sets, as predefined in TUAB and TUABEXB, respectively. To manage the statistical variance caused by initialisation and to improve the comparison between training and model configurations, we repeated the training and evaluation ten times for each model and reported the average of evaluation metrics across len independent runs (Bouthillier et. al.] 2021 Picard 2023: Wightman et al.] 2021). To ensure comparability with previous work, we evaluated the overall performance of model using the prediction for each recording. The model's prediction for a recording was calculated by averaging the outputs from all windows of that recording. Each recording was then classified as non-pathological or pathological based on its mean window probability. The performance of the different architect was evaluated using the following general classification metrics: accuracy, balanced accuracy, sensitivity, specificity and F2-score. We computed the evaluation metries for each run and then averaged the results across ten independent runs. In addition, we used the Mann- Whitney U test (Mann & Whitney 1947) to test for the statistical significance of the difference in performance metrics between the proposed Multi-BK-Net and each of the baseline architectures (H1). The null hypothesis (HO: No significant differences in the performance of Multi-BK-Nel compared to baseline architecture) was rejected with p-value of p < 0.05. To correct for multiple testing, we additionally performed Bonferroni correction [Bonferroni] 1936) with a = 0.05 for all performance comparisons involving our Multi-BK-Net and cach baseline architecture on the corresponding evaluation set. Statistical evaluation is based on 10 independent runs. In addition, we calculate the mean Receiver Operating Characteristic (ROC) curve and Precision-Recct Recall (PR) curves. To this end, ROC and PR curves were computed for each run, respectively. The curves from all 10 independent runs were then averaged pointwise to obtain the mean ROC and PR curves. We also calculated the area under the curve (ROC-AUC and PR-AUC) for each run and reported the mean and standard deviation across runs.
3.4 Performance Comparision to Baseline Architectures
In this section, we examine the performance of the proposed Multi-BK-Net compared with five baseline architectures on two predefined test datasets, TUAB and TUABEXB. The classification performance of the Multi-BK-Net and all baseline architectures on the TUAB is shown in Figure 2 With mean accuracy of 87.75%, the Multi-BK-Net shows the best classification performance. This was also statistically significantly better than the bascline models (p < 0.001, Mann-Whitncy U test). However, in medical diagnosis, such as EEG pathology classification, it is more important to identify all potential pathological cases (high re- call/sensitivity) even at the expense some false positives (lower precision). In this context, the F2-score is a suitable additional metric compare the different architectures. With a mcan sensitivity of 83.10% and a mean F2-score of 84.05%, Multi-BK-Net statistically significantly outperformed all other architec- tures (p < 0.001, Mann-Whitney U test), indicating better recall. Figure compares the classification
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Table 2: Mean sensitivity [%]] at 95% specificity. Standard deviation given in parentheses. For each architecture and test set, sensitivity at a fixed specificity of 95% was calculated for each run and then averaged across all ten runs (n=10).
Architecture
TUAB
TUABEXB
Deep4Net
48.97 (= 32.52)
66.99 (+ 6.80)
ShallowNe Net
55.87 (+ 36.60)
77.90 (+ 2.37)
TCN
77.94 (+ 0.59)
79.16 (+ 0.79)
EEGNet
70.48 (# 2.90)
52.78
(+ 34.64)
ChronoNet
78.89
(# 0.95)
40.34
(# 40.35)
Multi-BKS BK-Net
82.78
(+ 1.85)
84.25
(# 0.49)
performance of the Multi-BK-Net and five baseline architectures on the TUABEXB evaluation set. Again, the Multi-BK-Net outperformed the baseline models. In particular, Multi-BK-Net achieved higher mean accuracy of 87.01% on the dedicated test set, which was also statistically significantly higher than that of the baseline CNNs (p < 0.001, Mann- Whitney U test). We also observe that the Multi-BB-No achieved statistically significantly higher mean sensitivity (84.25%) and mean F2-score (85.17%) than each of the baseline CNNs (p 0.001, Mann-Whitney U test). Additional classification results are presented in Ap- pendix A.6.1 For more in-depth analysis of model performances, we report the classification performances subsets created based age and sex information in Section [A.6.5] Table 18 and Table 19 With minor exceptions (e.g., for patients younger than 25 years in the TUAB), Multi-BK-Net achieved higher mean accuracies and sensitivities than the baseline models across all subsets. In addition, we report mean ROC curves of the The Multi-BK-Net achieved the highest AUC of 0.874 and 0.870 on the TUAB and TUABEXB, respec- tively, indicating superior sensitivity and overall robustness. ShallowNet and TCN follow closely, whereas Deep4Net achieved the lowest. AUC. Figure 5 shows the mean PR curves of the Multi-BK-Net and all five baseline models the TUAB and TUABEXB test set, respectively, On both test sets, the Mu-titBB-Net achieved the highest AUC scores of 0.821 on the TUAB and 0.828 on the TUABEXB. This indicates that Multi-BK-Nel achieves superior discriminative performance and reliability compared to the baseline archi- tectures. To evaluate model performance in a clinically meaningful manner, we report sensitivity at a fixed specificity of 95% in Table 2 This metric reflects the true positive rate achieved when the false positive rate is constrained to 5%, corresponding to an acceptable level of false alarms in clinical practice. Unlike the overall AUC, which summarises performance across all thresholds, sensitivity at high specifieity provides -hresfidd-specific measure that aligns with real-world diagnostic requirements and allows for more relevant comparison between models. Compared with the baseline architectures, Multi-BK-Net achieved the highest mean sensitivities at a fixed specificity of 95% on both test sets (82.78% and 84.25%). The smaller standard deviations further suggest less variability between runs and thus more consistency, improved robustness and accuracy compared to baseline models. In general, our method achieved higher classification performance than five baseline architectures and was more effective in identifying pathological recordings.
and all five baseline models on the TUAB and TUABEXB test set in Figure }
3.5 Performance Comparision to Previously Reported State-of of-the-arttte art Approaches
To further evaluate the Multi-BK-Net, we compared its classification performance with that of other state- of-the-art end-to-end approaches reported in previous studies. More details on the previously published studies are provided in Appendix A.. Table 3 and Table 4 summarise the comparisons between our Multi-BK-Net and other methods on the TUAB and TUABEXB datasets, respectively. Overall, the Multi- BK-Net achieved performance comparable to better than other deep learning approaches across all three evaluation metrics, with small advantages in some settings. In particular, for the TUAB evaluation set, the following observations can be made by further analysis of the Table 3 First, compared lo the four single-scale CNNs, namely Deep4Net, ShallowNet, TCN and EEGNet (Darvishi- Bayazi et al. 2023 Gemein cet al. 2020 Khan et al. 2022. Kiessner et al.) 2023 2024 Schirrmeister et al. 2017a Western al. 2021 Multi BK-Net improves the mean classification accuracy by 1.85%, 2.24%, 1. 1.26% and 3.08%, respectively. Second, compared with the three architectures that use three convolutional scales, i.e., ChronoNet (Roy

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Published in Transactions on Machine Learning Research (12/2025)
TUAB
90
Accuracy [%)
86
84
82
80
87.75 (# 0.14)
83:22 (= 0.50)
83.54 (= 0.17)
86.05 (# 0.14)
84.35 (# 0.29)
85.80 (# 0.19)
Multi-BK-Net
Deep4Net***
ShallowNet***
TCN
EEGNe
ChronoNet
F2-Score [%]
80
70
65
84.05 (= 0.44)
71.03 (a 1.41)
79.08 (a 0.68)
79,83 (+ 0.16)
76.27 (# 0.79)
80.63 (# 0.26)
Multi-BK-Net
Deep4Net=**
ShallowNet**s
TCN Book
EEGNet***
ChronoNet ***
90
85
Sensitivity [%]
80
75
70
60
55
83.10 (= 0.49)
66.90 [= 1.66)
76.75 (# 0.89)
77.54 0.21)
73.41 (= 0.98)
78.81 (# 0.31)
Multi-BK-Net
Deep4Net***
ShallowNettts
TCN
EEGNet***
ChronoNet
Figure 2: Performance comparison between our proposed Multi-BK-Net and five baseline architectures on the predefined TUAB evaluation set. Each transparent marker represents the performance of a single run, and each larger, bold symbol represents the mean performance score averaged across ten independent runs (n=10). The mcan standard error is given in parenthesos. Stars indicate statistically significant differences in performance score between the corresponding baseline architecture and the Multi-BK-Net (Bonferroni- corrected two-sided Mann-W Soian-Whitney U test, p 0.05: *, p 0.01: ** 0.001: ***; n=10). For additional results, see Figure 8 Appendix A.6.1
et al. 2019a), XceptionTTmm model (Brenner 2024 and IRCNN (Wu et al. 2021), Multi-BK- improves the classification accuracy by 1.18%, 1.65%. and 0.65%, respectively. In other words, the Multi- BK-Net achieved classification accuracy of 87.75%, thus outperforming the best previously reported state- of-the-art performance of the IRCNN (Wu et al. 2021). Third, Multi-BK-Net also demonstrates higher classification accuracy than two pre-trained transformer- foundation models, BIOT (3.2M parameters) and LaBraM (396M parameters), both of which require substantial additional EEG data for pre-training and substantial computational resources due to their large number of parameters. Finally, perhaps the most clinically important finding is that, with mean sensitivity of 83.10%, Multi-BK-Net outperforms all other approaches, with mean sensitivity at least 3.26% higher (see Table 3) Furthermore, for the TUABEXB evaluation set, have the following observations from further analysis of Table 4 Compared with previous results [Kiessner et al. 2023; 2024), the Multi-BK-Not achieved higher mean accuracy (87.01%) and sensitivity (84.25%), representing improvements of at. least 1.66% and 2.76%, respectively. Thus, Multi- BK-Net achieved new staee-of-the-art results on the predefined TUABEXB evaluation set, setting a new benchmark. Additional results and a more detailed discussion are provided in Appendix [A.62] Overall, we observe that the Multi-BK-Net outperforms previously reported results on both datasets in terms of mean accuracy. In addition, Multi-BK-No achieved higher mean sensitivity of 83 84%, indicating improved
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TUABEXB
90
88
Accuracy [%)
84
82
80
78
74
87.01 t= 0.09)
82.13 (= 0.94)
84.50 [= 0.17)
84.98 (= 0.11)
83.22 (# 0.171
84.52 (# 0.24)
Multi-BK-Net
Deep4Net***
ShallowNet***
TCN
EEGNe
ChronoNet**
85
F2-Score [%]
80
75
65
60
55
85.17 (= 0.11)
71.17 (1: 2.02)
79.97 (+ 0.60)
81.02 (+ 0.20)
78.98 (1. 0.87)
81.99 (a. 0.29)
Multi-BK-Net
Deep4Net=**
ShallowNet**s
TCN =oo
EEGNet***
ChronoNet *ac
90
85
Sensitivity [%]
75
70
65
60
84.25 (= 0.16)
66.99 (2 2.27)
77.90 (= 0.79)
79.16 (# 0.26)
77.13 (# 1.22)
80.79 (= 0.33)
45
Multi-BK-Net
Deep4Net***
ShallowNet**s
TCN
EEGNet***
ChronoNet
Figure 3: Performance comparison between our proposed Multi-BK-Net and five baseline architectures on the predefined TUABEXB evaluation set. Conventions as in Figure 2 For additional results, see Figure Appendix A.6.1
performance for real-world modical applications, particularly for medical screening methods where high sensitivity is crucial for accurately identifying the presence of EEG pathology.
3.6 Ablation Experiments
To further validate the efficacy of the proposed Multi-BK-Net and to highlight the importance of the multi- scale and multi-branch components in the Multi-BK-Net, we performed several ablation experiments, where we a) compared the performance of the Multi-BK-NV to larger variants of the Deep4Net having the same width or approximately the same size as the Multi-BK-Net ('larger Deep4Net variants"); b) removed four of the five branches the Multi-BK-Nett (Single-Tere Temporal- Kernel Single-Branch Branch Net, STKSBNet, "single temporal kernel + single branch") while changing the number of filters from 35; and e) used five different temporal kernel lengths but one branch, i.e. concatenating the output of the five temporal convolution layers before the spatial convolution layer (Multi- teatt-Temmora--eeane temporal kernels + single branch'). The results of these experiments can be seen in Table 5 and Table 6 while Appendix A.6.4 provides more details on the ablation experiments, the size of the models (see Table 17) and a more detailed discussion of the results. In comparison lo the Mult-BK-Nell, 'larger Deep4Net variants' achieved statistically significantly lower mean accuracies and mean sensitivities (Table 5] indicating that the superiority of the Multi-BK-Net over the bascline models was not due the increased model size alone. When compared against our Multi-BK-Nett (Table G), the networks with 'single temporal kernel + single branch' and multiple temporal kernel + single branch" suffered from scatistically significant drop in
Single-Brance Net, MTKSBNet, "multiple
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ROC Curves
Mean ROC Curves
1.0
1.0
True Positive Rate (Sensitivity)
True Positive (Sensitivity)
0.4
0.4
Mult-BK--Net (AUC 0.874 0.005)
Mutt-BK-Net (AUC 0.870 0.003)
0.2
DeeptNet (AUC 0.817 0.016)
Deep4Net (AUC 0.820 0.028)
ShallowNet
Shall
0.845 0.005)
TCN (AUC 0.846 0.006)
TCN (AUC 0.850 0.003)
EEGNet (AUC = 0.832 0.010)
EEGNet (AUC 0.832 = 0.005)
ChronoNet (AUC
0.005)
ChronoNet (AUC 0.845 * 0.007)
0.0
0.2
0.6 Positive Rate (1 Specificity)
0.4
0.8
1.0
0.0
0.2
0.6 Positive Rate Specificity)
0.4
0.8
1.0
(a) TUAB evaluation set.
(b) TUABEXB evaluation set
Figure 4: Mean Receiver Operating Characteristic (ROC) curves of our proposed Multi-BK-Net and five baseline architectures the predefined test sets of the TUAB (left) and of the TUABEXB (right), averaged across 10 independent runs. Each curve represents the mean interpolated ROC performance across runs (n=10), with shaded areas indicating =1 standard deviation. The identity line is shown in grey dotted line. Mean + SD values of the area under the curve (AUC) are shown in the legend for each model.
classification performance, especially in terms of mean accuracies and mean sensitivitios, thereby highlighting the importance of these components towards classification.
3.7 Network Interpretation using UMAP Visualisation
To interpret the proposed network, extracted the learned features from the last convolution-poolic block of the Multi-BK-Net and visualised them using the Uniform Manifold Approximation and Projection (UMAP) (McInnes et al., 2018) method, a dimensionality roduction technique well-snited to visualising high-ddimensional data, such as feature representation vectors. Figure s shows the UMAP visualisation of the learned features. samples form distinct, compact clusters with minimal overlap, indicating that our proposed Multi-BK-Net is highly robust at classifying pathological samples and extracts well-classifiable features, which partly explains its good performance. Further analysis in Appendix A.6.3 reveals that compared to Multi-BK-Net, the three baseline models (Deep4Net, TCN and ChronoNet) tend to form smaller and more dispersed clusters, possibly indicating greater within-class variability or a less precise representation of the pathological class. These results further align with the quantitative performance metrics, such as accuracy and sensitivity, shown in Section 3.4 Overall, the UMAP visualisation of the learned features further supports the efficacy of our method in extracting features and identifying pathological samples.
shown in the plot,, the representations of the pathological and non-pathological
3.8 Network Interpretation using an Amplitude Gradient Analysis
In this section, we investigate how sensitive the Multi-BK-Net's pathological prediction is to amplitude changes in the imput. Previous neurophysiological studies and glossaries have described the most common abnormal EEG patterns, for example, amplitude changes within multiple frequency bands (Amin et al.
2023 Emmady & Anilkumar 2023 Hoppe 2018 Kane al. 2017 Medithe Nelakuditi 2016 Tatun & Hence, amplitude changes useful for F.G classification. To determine how sensitive
William 2021.
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Mean Precision-Recall Curves
Mean Precision-Recall Curves
1.0
1.0
Precisian (Positive Predictive Value)
0.9
Precision (Positive Predictive Value!
0.7
0.7
0.6
Multi-BK-Net (AUC - 0.821 # 0.007)
MultiBK-NNet (AUC - 0.828 0.005)
Deep4Net (AUC 0.757 = 0.028)
Deep (AUC 0.806 = 0.024)
ShallowNet
ShallowNet 0.808
TCN [AUC 0.789 0.009)
TCN (AUC 0.812 0.005)
EEGNet (AUC 0.795 0.010)
EEGNet (AUC 0.790 0.012)
ChronoNet (AUC 0.798 0.008)
ChronoNet (AUC
0.009)
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Recall (Sensitivity)
Recall (Sensitivity)
(a) TUAB evaluation set.
(b) TUABEXB evaluation set
Figure 5: Mean Precision Recall (PR) curves of our proposed MultiBB-Nett and five baseline architectures the predefined test sets of the TUAB (left) and of the TUABEXB (right), averaged across 10 independent runs. Each curve represents the mean interpolated PR performance across runs (n=10), with shaded areas indicating #1 standard deviation. Mean + SD values of the area under the PR curve (PR-AUC) are shown in the legend for each model.
TUAB
TUABEXB
15
Multi-BK-NN -Ke Component 2
10.
Component 2
10
Nomal Pathological
-10
Norma Pathological
-15
-2.5
0.
2.5
5.0
7.5
10.0
12.5
-10
-5
10
15
20
25
Component 1
Component
Figure 6: UMAP visualisation of feature representations learned with Multi-BK-Not for pathological and non-pathological recordings on the predefined TUAB (left column) and the TUABEXB (right column) evaluation sets. Pink dots represent the pathological class, while green dots represent the non-pathological class.
the Multi-BK-Nel prediction is to amplitude changes across different frequency bands, we calculated the gradients of the output with respect to the input amplitudes, ie., the recordings from both the TUAB and TUABEXB evaluation sets (Ancona
2018 2019 Gemein al. 2220 [ccirrmeister et al. 2017a). The gradients were then grouped by pathological status and averaged over ten runs. The gradients of the model prediction with respect to the input amplitudes linearly approximate how the class prediction responds
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Table 3: Performance comparisons of the Multi-BK-Net with previously reported state-of-the-art deep learn- ing methods on the TUAB datasct. Accuracy (ACC), sensitivity (SENS) and specificity (SPEC) scores are given in %, n.a.: not available. Results marked with xa are reported as balanced accuracy. Publicly available reimplementation. Studies marked with used data augmentation. Studies marked with used only the first minute of each recording for performance evaluation. Studies marked with b used cropped training strategy as described by Schirrmeister et al. (2017a b). Studies marked with 2 reported mean re- sults averaged across several independent runs. Results marked with f are the performance on TUAB using the TUABEXB training set (Kiessner et al. 2023) instead the TUAB training set. Results marked with ş are the performance on TUAB using the TUABCOMB (Kiessner et al. 2024] for training. Results marked with are averaged across ten runs (n=10).
Study
Architecture
ACC [%]]
SENS|
SPEC [%]
Schirmeesstr et al.
2017a
Ø
Deep4Net
85.40
75.12
94.13
ShallowNet
84.50
77.32
90.53
Roy et al. 2018 <
Ø
ID-CNN RNN
82.27
n.a.
Roy et al. 2011aa Ø
ChronoNet
86.57
n.a.
n.a.
Gemein ct al.
22020 Ø
TCN
86.10
79.70
91.60
Deep4Net
84.60
75.90
91.90
ShallowNet
84.10
79.70
87.90
EEGNet
83.40
72.10
92.90
Western et al. 20021 Ø
Deep4Net
85.90
77.00
93.30
Wu et al. (0221) < Ø
IRCNN
87.10
n..a.
n.a..
Khan et al. (2022)
ChronoNet πI
81.00
11.8.
11.8.
ShallowNet
85.00
n.a.
11.a.
Deep4Net.
84.00
n.a.
Hybrid CNN+LSTM
85.00
Darvishi-Bayer et al. 2023
x0
Deep4Net
81.64
ShallowNet
82.40 bX
n.a.
n.a.
TCN
81.69 bx]
n.a.
n1.a.
EEGNet
81.40 ba
n.a.
n.a.
Kiessner et al.
2023
b Ø
Deep4Net
85.51
75.95
93.53
ShallowNet
84.13
79.84
87.73
TCN
85.72
78.81
91.53
EEGNet
84.67
72.94
94.53
Deep4Net f
84.89
70.95
96.60
ShallowNet t
85.51
77.30
92.40
TCN i
86.49
77.06
94.40
EEGNet f
83.51
77.22
88.80
Yang et al. (2023] ) b
BIOT
79.59 bx
n.a.
n.a.
Kiessner et al. 2024 h
Deep
85.29
73.73
95.00
ShallowNet
85.22
76.83
92.27
TCNІ
86.09
77.70
93.13
EEGNett
83.80
78.41
88.33
Brenner al. 2024
XceptionTime model
85.10
85.70
84.70
Jiang et al. 2024
LaBraM-Huue
82.58
n.a.
n.a.
Proposed methodø
Multi-BK-Net 87.75
83.10
91.93
to changes in the inputs. For example, if the amplitude at a certain channel is changed by a value x, the model output for the class pathological will change approximately by gradient*x. The resulting patterns lincarly approximate how changes in input amplitude affect the model's prodiction of pathology, indicating which changes in input amplitude are most informative or interesting for the task of classifying general EEG pathology. In addition, analysed the clinical E.G reports included in the TUAB and TUABEXB datasets.
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Table 4: Performance comparisons of the Multi-BK-Net with previously reported state-of-the-art deep learn- ing methods on the TUABEXB dataset. Accuracy (ACC), sensitivity (SENS) and specificity (SPEC) scores
are given in %, n.a.: not available. Conventions in Table 3
Study
Architecture ACC [%)
SENS[%]
SPEC [%]]
Kiessner et al. 2023
Ø
Deep4Net
83.94
71.99
95.75
ShallowNet
84.47
79.32
89.55
TCN
83.73
77.49
89.91
EEGNet
82.38
80.79
83.96
Kiessner et. al.
2024
2
Deep4Net $
85.35
75.27
95.32
ShallowNet
84.58
79.79
89.31
TCN;
85.08
79.12
90.98
EEGNee
82.65
81.49
83.80
Proposed methodø
Multi-BK-Net
87.01
84.25
89.73
Table 5: Ablation experiment "larger Deep4Net variants'. Comparison of the classification performance of Multi-BK-Net with larger Deep4Net variants on the prodefined TUAB and TUABEXB evaluation sets. Deep4Net as defined in [Schirmeisser et al. (2017a bb. DeepdNee33 (No. start filters: 35); Deep4NetA77 (No. start filters: 47): Deep4Net48 (No. start filters: 48). Performance metrics are averaged across ten independent runs (n-10). The mean standard error is given in parentheses. ACC: Accuracy, BACC: Balanced accuracy, F2: F2-score, SENS: Sensitivity, SPEC: Specificity. Stars indicate statistically significant differences in performance between the corresponding model variant and the Multi-BK-Net (Bonferroni- corrected, one-sided Mann-Whitney test, < 0.05: < 0.01: #* < 0.001: *** n=
Dataset
Architecture
ACC [%]
BACC [%]
F2 [%]
SENS [%]
SPEC [%]
TUAB
Deep4Net
83.22***
81,92***
71.03*** ***
66.90***
96.93
Deep4Net35
84.78***
83.63***
74.21 ***
70.40***
96.87
Decp4Net47
84.89***
*3.90***
75.72*** ***
72.46***
95.33
Deep4Net48
84.96***
83.94***
75.52
72.14
95.73
Multi-BK-Net
87.75
87.36
84.05
83.10
91.93
TUABEXB
Deep4Net
82.13***
82.04***
71.17***
66.99***
97.09
Deep4Net335
83.52***
83.4 83.45
74.59
70.88*+*
96.02
Deep4Net47
84.50***
84.44***
76.73*
73.42***
95.46
Decp4Net448
83.87*
83.80**
75.14
71.52***
96.09
Multi-BK-Net
87.01
86.99
85.17
84.25
89.73
Figure 7 shows scalp maps of the amplitude gradients across different frequency bands for the pathological class. The largest absolute values are observed in the delta (0-4 Hx), theta (4-7 Hx) and alpha (8-12 Hz) frequency bands, indicating that the model's pathological prediction is most sensitive to amplitude changes in these specific frequency bands. In particular, in the delta and theta frequency bands, peaks corresponding to positive gradients are observed at the temporal (T3, T4) electrode locations. Specifically, an increase in amplitude in the temporal areas led to an increase the prediction of the pathological class. This localised pattern is also consistent with clinical EEG reports, which often state that recordings are classified pathological due to the presence of non-epileptiform abnormalities, such as diffuse focal slowing in the theta delta bands (Emmady & Anilkumar 2023 Nayak & Anilkumar 2020 Moreover, the clinical EEG reports often cite the "absence of postcrior dominant rhythm" as reason for pathological labelling. Similarly, a negative gradient peak in the alpha band was observed in the occipital brain region, indicating that the predicted probability of the pathological class decreases as the alpha-band amplitude at occipital electrode locations incrcases, which, in turn, suggests the presence of the PDR. Overall, the results align with current neuroplysiological knowledge on pathological EEG patterns, pathological patterns identified by human experts in clinical FEG reports, well recent studies that implicate the role of low-frequency
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Table 6: Ablation experiments "single temporal kernel + single branch' and "multiple temporal kernels + single branch'. Comparison of the classification performance of STKSBNet variants, MTKSENet variant on the predefined TUAB and TUABEXB evaluation set. STKSBNet uses a single kernel and STKSBNet7: kernel size of 7) and single branch in the first block. MTKSBNet uses five different size (e.g., kernel sizes, but one branch in the first block. Performance metrics are averaged across ten independent runs (n=10). The mean standard error is given in parentheses. Conventions as in Table
Dataset
Architecture
ACC [%]
BACC [%]
F2 %
SENS [%)
SPEC [%
TUAB
STKSBNet3
84.71 ***
84.33 *
81.00
79.92**
88.73#*
STKSBNet7
84.13***
83.76***
80.46**
79.44**
88.07**
STKSBNet10
84.96***
84.57***
81.20
80.08*
89.07**
STKSBNet13
86.45**
85.99**
82.17*
80.71**
91.27
STKSBNet25
86.27**
85,81** **
82.00
80.56**
91.07
eTKSBNet200
86.38***
85.92***
82.09*
80.63*#
91.20
MTKSBNet
86.59**
86.16**
82.55*
81.19*
91.13
Multi-BK-Net
87.75
87.36
84.05
83.10
91.93
TUABEXB
STKSBNet3
82.73
82.72*4*
81.48***
80.93***
84.52**
STKSBNet7
84.85***
84.83***
82.60 **
81.54***
88.12**
STKSBNe010
86.09**
86.07*
83. 83.76*#* *
82.62***
89.51
STKSBNet13
85.94**
85.82**
83.58
82.44**
89.40
STKSBNet25
85.95***
85.93***
83.65
82.53***
89.33
STKSBNeL200
86.16**
86.14**
83.62
82.38***
89.91
MTKSBNet
86.46
86.44
84.26*
83.19*
89.69
Multi-BK-Net
87.01
86.99
85.17
84.25
89.73
oscillations in CNNs' classification of general EEG pathology (Gemein et al.
2020 Nahmias & Kontson
2020
Schirrmeister et al. 2017a).
1e-7
0-4 Hz
4-7 Hz
8-12 Hz
13-30 Hz
30-50 Hz
3.428
Multi-B B-ii-BK-Nett Net
1.714
Gradients
Pathological
0.000
00
Tata
-1.714
-3.428
Figure 7: Gradients with respect the input amplitudes for the pathological class with respect to different frequency bands for the Multi-BK-Net the combined TUAB and TUABEXB evaluation sets. Delta (0-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz) and low gamma (30-50 Hz) frequency bands. The red colour indicates positive gradients, while the blue colour indicates negative gradients for the pathological class. Scalp maps are scaled to the absolute maximum value. However, the amplitude gradient results reflect the behaviour of the trained model, and any interpretation of the data itself must be made carefully [Schirrmeister et al. 2017b
3.9 Insights from the Textual EEG Reports
To better understand which EEG recordings are challenging for our Multi-BK-Net to classify, we qualitatively analysed the textual EEG reports each recording that was misclassified. In particular, for each run of our Nulti-BK-Net, we identified all falso-positive and falso-negative recordings and extracted the corresponding EEG reports. In total, 147 pathological recordings (TUABEXB: 116 recordings; TUAB: 31 recordings) were falsely classified as non-pathological (false negatives, FN), while 139 non-pathological recordings (TU-
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ABEXB: 113 recordings; TUAB: 26 recordings) were falsely classified as pathological (false positives, FP). We performed a qualitative analysis of the clinical EEG reports independently for both FN and FP, focusing on the impression and clinical correlation sections.
The qualitative analysis of the FN clinical EEG reports reveals several commonalities due to which the Multi-BK-Net might incorrectly classify them as non-pathological: i) Failure to detect subtle, small, mild abnormalities: Most notably, terms such as "small amount", 'very mildly' or "subtle" were used to describe EEG abnormalities that, while not overtly prominent, were still significant enough to be noted clinically, It is possible that this subtlety of change caused the classification errors ('a small degree of background organisation', 'this is very subtle abnormal EEG', "some subtle focal slowing", 'this is a mildly abnormal EEG"). ii) Missing single, infrequent or intermittent events: In some cases, single or infrequent events were the reasons why an EEG recording was rated abnormal by the human expert ('A single generalized discharge noted in stage IT sleep.", "infrequent, shifting focal slowing present.', 'rare bursts of shifting slowing"). Abnormalities described as 'infrequent" or "single" could be missed by CNNs, as the model might average them out or treat them as statistical outliers. It is also possible that these events are not present in the data, as we used only the first 21 minutes of the recordings for training and evaluation (see Section 3.1) iii) Misinterprotation of 'nonspecific' abnormalitics: Within the FN EEG reports, many abnormalities listed in the impression section are described as 'etiologically nonspecific'. This implies that even while an abnormality is present, it does not clearly indicate a specific disease. It. is therefore possible that the CNN might struggle to classify these less-specific but still abnormal findings as within the range of normal variability, resulting in a false negative prediction. iv) Lack of clinical context: In EEG reports for FN recordings, clinicians include additional information to help interpret EEG patterns as pathological, such as patients' ago, current modications, clinical history. For example, experts often correlate observed EEG patterns with the patient's 's clinical history. Thus, some nonspecific abnormalities are rated as indicating pathology based on the climical history of the patient ('The overall record is relatively unremarkable. The 7.8 Hz activity is of unclear significance and may represent a normal variant. Given the past history of neurologic pathology, however, it may also represent evidence of an underlying diffuse disturbance of cerebral function."). ") In contrast, without a clinical context, the CNN classifies these recordings as normal. Also, the term 'age" is consistently used as factor in determining EEG patterns as pathological, illustrating an age- dependent interpretation ('very mildly abnormal EEG for an adult of this age." "This EEG is just outside of the range of normal for a subject of this age"). This suggests that certain physiological variations are considered pathological in specific age groups. In addition, clinicians often list EEG patterns that could be due to medication ('The minimal excess theta identified above is nonspecific, but could be associated with this patient's medications." 'TThe findings described above could be due to this patient's medications. " 'The slowing of the alpha rhythm may be due to the patient's medications.") It is possible that CNN can recognise medication-indueed patterns (Nahmias et al. 2020) and thus classify recordings as von-pathological. Moreover, reports often mentioned terms such 'sleep' or "drowsiness", indicating that the experts used the patient's 's (partial) sleep during the recording to interpret the EEG. During drowsiness, eye movement artefacts, such as blinks and slow or roving eye movements, are commonly observed and can complicate EEG analysis (Mohammedi et al. 2023
Qualitative analysis of FP clinical EEG reports reveals several commonalities that may cause Multi-BK- Net to misclassify normal recordings as pathological: i) Limitations of routine EEG: The primary reasons listed for "normal" classification are often 'the absence of definite epileptiform activity or significant focal abnormalities during routine recording" Frequently, clinical suspicion remains high despite normal routine EEG, and further comprehensive evaluation is recommended in the clinical EEG reports. The FP reports frequently emphasised the nuances and limitations of EEG interpretations, particularly when dealing with conditions such as epilepsy or seizures. While reports often conclude the findings described as "essential normal' or 'within normal limits", they consistently include reservations and recommendations for further clinical correlation ('Midly diffuse cerebral dysfunction cannot be ruled out", A normal routine EEG does not exclude the diagnosis of epilepsy.', "This is an essentially normal awake and drowsy 1-hour EEG, though mild diffuse cerebral dysfunction cannot be ruled out."). ii) Misinterpretation of nonspecific findings: Terms like 'etiologically nonspecific' mentioned in the EEG reports indicate abnormal findings ("There were no specifically pathological sharp waves observed.', "The amplitude is mildly high for the patient's 's age, but this is nonspecific.") '). The CNN might detect these abnormal patterns but cannot recognise their nonspecific
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nature, leading to false-positive classification. iii) Lack of clinical context: As noted above in our FN analysis, the FFGG reports of the FP also frequently include contextual information about the patient's 's age, state (awake, drowsy, sleep), medication and clinical history to classify the recording as non-pathological. For example, using information from the patient's clinical history, experts correlate EEG patterns with known skull defects and rate the breach rhythın as a benign variant secondary to structural changes. In contrast, without the contextual information of a pre-existing skull defoct, the CNN might classify these patterns as abnormal rather than a benign variant. Also, EEG patterns are rated as benign variants based on the patient's ' age ("EEG within normal limits for age', 'within in normal limits', 'EEG at the limits of normal for subject of this age"). While these are not explicitly listed as reasons for an abnormal EEG, reports often mention 'mild diffuse cerebral dysfunction' or "minor shifting slowing in drowsiness', indicating that even subtle non-epileptiform findings have been noted and are frequently correlated with the patient's history ("Occasional mild independent bitemporal slowing is not considered abnormal in the patients above 65 years of age."). While human experts rate abnormalitics as clinically insignificant based on additional context, the CNN might identify them as abnormal patterns in the absence of such context. This further supports the assumption that the lack of clinical context is crucial factor in the CNN's misclassification of EEG recordings. Information about sleep and drowsiness indicates that experts used the fact that the patients were (partially) asleep during the recording to interpret the EEG ('EEG at the limits of normal primarily in drowsiness." " "The EEG is remarkable for excess drowsiness. However, when the patient, can be awakened, her pattern is within normal limits", 'This EEG is compatible with a sedated, sleep pattern."). '). During drowsiness, physiologically normal but diagnostically challenging changes occur that can closely resemble pathological findings, such as slowed background frequencies and increased slow eye movements, complicating automated visual EEG analysis (Amin et al., 2023 Strijbis et al., 2022p. In contrast, the CNN lacks access to patient information, including the patient's s state, age, medications, and clinical history. As result, the model may misclassify age-appropriate or medication-related variations as pathological abnormalities. iv) Presence of large artefacts: In some FP reports, the presence of large artefacts in the EEG obscuring significant portions of the EEG channels is given as the reason why features are under-rocognised or cannot be reliably identified and thus additional studies are recommended ("This EEG include electrode artifact on the right as well as an inability to stimulate the patient adequately. Consequently focal features may be under recognised '). It is possible that the Multi-BK-Net can recognise abnormal EEG patterns despite the presence of large artefacts (Kalita et al. 2024 Crepkema-Clostermnnn et al. 2025
Overall, this qualitative review suggests that the Multi-BK-Ne struggles to identify context-dependent abnormalities. Clearly, our Multi-BK-Net, which was only trained on the EEG signal, does not have access to this contextual information. This puts the model at a disadvantage relative to clinicians, emphasising the potential benefit of incorporating contextual information, such as age and vigilance, into the training and classification processes. Moreover, integrating ratings from multiple experts to train and evaluate the model using averaged ratings, so-called 'fuzzy labels', which represent a probability of pathology, might further enhance performance and better reflect diagnostic uncertainty in EEG interpretation (Stephansen ct al., 2018). For example, converting classification tasks to probabilistic predictions using fuzay labels for ambiguous samples (Xu et al.] 2024) can help to identify when a model is less specific, guiding clinicians on which samples might require additional mamual review (Kang et al. 2021 J. For a more detailed discussion,
see Section !
3.10 Cross-Institutional Generalisation Performance
Although TUABEXB is larger and more diverse than the TUAB, both datasets originate from the same institution, the Temple University Hospital (TUH), raising concerns about noss-institutional generalisa- tion. At the same time, the possibilities to empirically investigate generalisation performance across multi- institutional datasets remain limited duc to the lack of publicly available datasets labelled for general EEG pathology classification. One open-source dataset for general EEG pathology classification that is not derived from the TUH and that contains the same subset of 21 EEG channels as the TUAB and TUABEXB dataset is the NUST-MH-TUKE collected from a South Asian demographic, comprising 2,417 unique patients at the Pak-Emirates Military Hospital. The NMT training set is imbalanced, with 325 pathological and 1907 non-pathological recordings.
(NMT) EEG dataset (Khan al. (022). The NMT contains 2,417 recordings
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Table 7: Cross-institution generalisation performance comparison between our proposed Multi-BK-Nee and five bascline architectures, which were trained on TUH data and applied to the predefined NMT evaluation set. Mean scores are averaged across ten independent runs (n=10). ACC: Accuracy, BACC: Balanced accuracy, F2: F2-score, SENS: Sensitivity, SPEC: Specificity. The standard deviation is given in parentheses. Stars indicate statistically significant differences in performance between the corresponding baseline model and the Multi-BK-Ne (Bonferromi-corrected, two-sided p < 0.001: ***; n=10)
U test, p < 0.05: *, p 0.01: **
Architecture
ACC [%]
BACC [%]
F2 [%]
SENS [%] 82.78 32375()35.51)
SPEC [%] 17.74(=37.54)
Deep4Net
49.73 (2.25 ****
50.26( ***
69.6( 69.6(=28.28)
ShallowNet
52.02(11..99)
52.62(11..06 ***
75.85 (6.85(=16.16)) 16)
89.0(124.24)
16.24 (134.44)
TCN
54.75 8884)**
54.00((12.94) th*
11.86(=10.32))
10.0(19.04)**z *
98.06(=3.39) *88
EEGNet
(0.82(10.0))) 10)))
50.0( *+*
0.0(#0.0) +R
0.0(
100.0
ChronoNet
57.76(14.61)
13.62(=1
33.62(
84.1
Multi-BK-Ner
67.32 (+2.3) 67.46 (+2.17) 73.12 (+3665)
75.89 (+6.18)
59.03 ( 10.28)
The NMT evaluation set contains 90 pathological and 95 non-pathological recordings (for more information about the dataset, please refer to Khan et al. (2022) ).
To evaluate cross-instilutional generalisation performance, we assessed the classification performance of the Multi-BB-Nee and all baseline models trained on TUABCOMB on the predefined NMT evaluation set. For more details on performance evaluation, see Section 3.3 We preprocessod the NMT evaluation set data as described in Section 3.1 We observed significant performance degradation across all architectures when applied to the NMT dataset, as shown in Table 7 but less so for our proposed Multi-BK-Net compared to the baseline CNNs. Compared with the five baseline architectures, Multi-BK-Net achieved the highest ac- curacy, balanced accuracy, and F2-score. Although Deop4Net and ShallowNet demonstrated slightly higher sensitivily, their specificity was considerably lower. Both models achieved an accuracy of around 50% sug- gesting they perform at chance level. Overall, all models performed significantly worse on the NMT than on the TUAB and TUABEXB evaluation sets. This also accords with previous studies, which showed that the performance of deep learning approaches trained on the TUAB degrades significantly when they are evaluated on the NMT without any prior exposure to the target dataset and vice versa. (Darvishi-ayaazi ct al. 2023 Khan et al., 2022). Khan et al. (2022) have evaluated the classification performance of Decp4Net and ShallowNet on NMT after training the models on the TUAB dataset and observed a degradation in performance (48-50% accuracy), indicating a distribution shift. In addition, the authors discussed several factors that may have contributed to this cross-institutional variability, including differences in demographies and the use other hardware for data collection (for a more detailed discussion, please refer to Khan el al.. (2022)). Similarly, Darvishi-Baaavi et al. (2023) observed performance degradation in four CNNs trained on the TUAB when applied to the NMT evaluation set, and vice versa. On the other hand, for training and testing models on the NMT, the accuracies are in the range of 70-80% Marvishi-Bayazz et al. 2023 Khan et al. 2022). Table S summarises the comparison of cross-institutional generalisation performance between our Multi-BK-Net and other methods reported in previous studies. When compared with pre- viously reported results shown in Table S. the Multi-BK-Net demonstrated superior accuracy of 67.32%, compared with the tross-nnsiiuutinaal generalisation performance reported by other studies, which ranged from 45.00% to 63.04%. Together, these results suggest that current deep learning- basod approaches are at risk of institutional bias, which limits their generalisability and could lead to performance differences based demographics, technical, and environmental factors [Bomatter & GGuuk] 2025 Saab et al., 2020 Tjepkema-
Cloostermans et al. 2025). Furthermore, Khan et al. (2022) demonstrated that fine-tuning can mitigate the impact of cross- conss-nnttitutinnll variability. Darvishi-Bayazi et al. (2023) also observed that pretraining models on the TUAB and fine-tuning them on NMT enhances the performance on the NMT evaluation set, but at the same time significantly reduces the performance on the TUAB evaluation set, indicating a negative transfer. This shows that there still room for improvement in transferring knowledge from TUH to NMT. Overall, these results emphasise the need for strategies to mitigate these challenges, as well as for more publicly available datasets from multiple institutions. We discuss this topic further in Section 4
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Table 8: Cross-institttonall generalisation performance comparisons of the Multi-BK-Net with state-of- the-art deep learning methods previonsly reported, which were trained on TUH data and applied to the NMT dataset. Accuracy (ACC), sensitivity (SENS) and specificity (SPEC) scores are given in %, n.a.: not
available. Conventions as in Table 3
Study
Trained
Architecture
ACC [%]
SENS[%]
SPEC
[%]]
Khan et al. 2022
TUAB
Deep4Net
48,00
n.a.
n.a.
TUAB
ShallowNet
45.00
n.a.
n.a.
Sarvishi-Bayaze et al. (2023
XØ
TUAB
DeepANet.
62.7764
n.a.
n.a.
TUAB
ShallowNet
62.02
n.a.
n.a..
TUAB
TCN
63.04b4
n1.a.
n1.a.
TUAB
EEGNet
61.45e4
n.a.
Proposed methodø
TUABCOMB
Multi- -eett
67.32
75.89
59.03
4 Discussion and Conclusion
In this work, we demonstrated that through the incorporation of a) multiple temporal kernel lengths based five clinically relevant frequency bands and b) multiple parallel branches within the first convolution- pooling block, our proposed CNN can outperform five bascline methods in classifying general EEG pathology, while being more effective al capturing the heterogeneity of pathological EEG recordings than their single- scale counterparts. To that end, our Multi-BK-Net extracted task discriiiciiiiaaivee long-ten torg-term and short-term spatiotemporal EEG features for general EEG pathology classification, achieving higher performance than comparable deep end-to-end state-of- the-art methods on two public datasets. The ablation experiments further highlight the efficacy of the multi-branch, multi-scale components in our proposed CNN architecture. Additionally, the UMAP visualisation of the learned features shows that the Multi-BK--Ne forms more compact and distinct features than the baseline CNNs, which partly explains the good performance of this architecture. Moreover, the correspondence wetn the model's sensitivily to localised patterns of amplitude changes in different frequency bands and both current neurophysiological knowledge of pathological EEG patterns and the pathological patterns identified by human experts in the clinical EEG report increased the reliability of the method.
This work addresses significant gap in CNN approaches that has hindered their development into robust, broadly applicable deep learning- based general EEG pathology classifiers. While CNN-based methods have shown state-of-the-art performance on the task of general EEG pathology classification (Darvishi Bayazi et al. 2023 Gemein et al., 2020 Khan et al. 2022 Kiessner et al., 2023 2024 Van Leeuwen et al.) 2019
Western et al. 2021 Wu et al. 2021). their classification performance is limited mainly due to the high intra-
and inter-subjecl variability of the EEG signal (Lashgari et al. 2020 Nahniaa el al. 2019 Schirrmeister
et al. 2 the heterogeneity of general EEG pathology patterns (Emmady & Anilkumar 2023 Nayak & Anilkumar 2020) and the small size and and limited diversity of the TUAB dataset commonly used for
training and evaluating the networks (Kiessner et al. 2023 Poziomska et al. 2025). We demonstrated here that an appropriately constructed CNN. incorporating a multi-scale and multi-branch network design, can achieve more accurate and reliable classification performance than state-of-the-art CNN methods. This is evident from the high mean evaluation accuracies and sensitivities achieved by our method on two public datasets from the Temple University Hospital, as well as from our ablation experiments, thus highlighting efficacy in classifying general EEG pathology. Our model addresscs the challenges inherent to EEG, including its heterogeneity.
An improvement in evaluation sensitivity demonstrates further practical gains of our method. Our proposed Multi-BK-No achieved mcan scnsitivitics of 83.10% and 84.25% on the TUAB and TUABEXB, respectively, outperforming all other approaches by 3.26% and 2.76%, while at attaining high classification accuracies. In comparison to the baseline architectures, Multi- Malti-BK-Nett achieved the highest mean sensitivities at a fixed specificity of 95% on both test sets (82.78% and 84. 84.25%). The significance of this result lies in applications such as deep barming-based EEG classification and clinical decision support systems in clinical practice, for which high, robust sensitivity is erucial to ensure accurate identification of EFG pathology. In deep learning-
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based clinical EEG analysis, high accuracy and sensitivity of most significant importance because they directly translate into improved patient outcomes, reduced diagnostic errors, and enhanced clinical efficiency. High sensitivity ensures that critical events, such as seizures, are not missed, preventing potential patient harm and delays in treatment [Tešmantas & Alzbutas] 2020 Wang et al. 2023 High accuracy is crucial for reliable diagnosis and proper palient. management. Thus, even minor improvements in these metrics are valuable because they can significantly reduce false detections, achieve expert-level agreement, and expedite physician analysis, making the systems more trustworthy and practical for everyday clinical use (Hogan) et al., 2025 King-Stephens/ 2024 Saab et al. 2020). Such advancements can also lead to better trade-offs between sensitivity and false-positive rates, a allowing clinicians to tune the system for optimal performance in specific scenarios (Wang et al. 2023). To be applied in real-world clinical practice, the proposed method must be reliable and utilise robust, well-classifiable features to achieve high performance across a diverse patient population. That is why the evaluation on a larger, more heterogeneous dataset was central in this work. Morcover, we interpreted the model using an amplitude gradient study, which shows that the model's pathological prediction is sensitive to localised patterns of amplitude changes, aligning well with current neurophysiological knowledge of abnormal EEG patterns (Emmady & Anilkumar 2023 Kane et al. 2017; Nayak& & Anilkumar 2020) and pathological patterns mentioned in clinical EEG reports. In addition, the UMAP visualisation of the learned features further supports the efficacy of our method in extracting features and identifying pathological samples.
While our findings show promise, the broader applicability of these results is subject to certain limitations. For instance, the current study is based on data obtained from a single institution, i.c. Temple University Hospital (TUH). Although using public datasets such as the TUH EEG Corpus (TUEG), which was collected directly in a clinical setting over 14 ycars and exhibits wide variation in essential parameters such as patient age and diagnosis, is vital first step in developing deep learning methods that represent the variability of EEG data in real-world applications, it is important to acknowledge several significant limitations of single- institution datasets, such as the potential for institutional bias and demographic performance disparities, that can hinder the effectiveness and real-world applicability of the models. Specifically, models trained solely on data from a single institution may exhibit reduced performance when applied to data from other institutions due to distribution shifts, i... differences in patient demographics, acquisition protocols, or equipment (Darvishi-Bayee Bayazi et al. 2023 Khan et al.] 2022, Poziomska et al., 2025). These discropancies between training and Test. data can signilicantly degrade the model's 's accuracy and reliability in practical applications /Vishwanath et al. 2023 In addition, limited data diversity in the training data can prevent the model from learning features that are broadly applicable across a wide range of clinical scenarios, thereby restricting its utility to a specific context, institution, or patient population. Thus, models trained on single- institutional data may lack the robustness needed to handle the inherent variability in real-world clinical settings (Li et al.) 2025p. Consequently, the reported performance might not accurately reflect the model's ability to classify EEG data from new, unseen patients institutions, thereby overestimating the model's clinical utility (Kamrud et al. 2021 Del Pup et al. 2025 Shafiezadeh et al. 2023). Indeed, these challenges have already been demonstrated in evaluating cross-institutional generalisation performance between datascts from Temple University Hospital and the NMT dataset from Pak-Emirates Military Hospital (Darvishi- Bayazi et al.] 2023 Khan et al. 2022 see also Section 3.10p. Bayyish et al. (2023) highlighted the challenges associated with negative transfer, while also emphasising key components for overcoming distribution shifts and potential spurious correlations. Building on the insights into cross-dataset transfer learning provided by Darvishi-Bayavi et al. (2023), further studies should explore additional transfer learning strategies. To this end, diverse datasets from multiple institutions and countries are needed to better understand institutional bias and the risks of demographic disparities, and to develop robust and widely applicable models. Also, while our proposed method demonstrates strong performance on a large and diverse public dataset, the absence of validation in real-world clinical settings (e.g., clinician feedback or pilot deployment) is another limitation, thereby constraining conclusions about the model's practical adoption or usability in routine clinical workflows. The issues of cross- insssssistttttinall validation and practical deployment in real-world clinical settings are intriguing and, therefore, an essential next step to establish the real-world utility and broader applicability of multi-branch, multi-kernel CNNs for general EEG pathology classification.
Despite its limitations, this study certainly contributes to the advancement of deep learning based general EEG pathology classification. This paper describes our efforts to improve the classification performance
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and robustness of CNNs for EEG pathology classification on diverse patient population. To this end, we proposed the Multi-BK-Net, a novel network architecture with multiple temporal kernels and branches in the first block to address the challenges posed by variability and heterogeneity inherent in EEG signals. The enhanced performance, combined with promising capabilities for handling EEG heterogeneity, suggests that the Multi-BK-Nel has significant potential to improve the practicality of deep learning- general EEG pathology classification. Overall, our results demonstrate the efficacy of multi-branch, multi-scale CNN solutions for classifying general EEG pathology by discovering task-relevant features in recorded brain activity, thereby better capturing heterogeneity across pathological samples.
Further performance improvements could be achieved by exploiting advances in the training of CNNs. Some of these include the incorporation of (self-)attenidd
methods (Altaheri et al.) 2023a Liu et al. 2024 Petit et al. 2021) or increasing the size and diversity of the data by using data from multiple sources (Aerts et al.
2017 Poziomska et al. 2025 Schinkel et al. 2023). p. In addition, the use of medications by most patients
poscs an additional challenge for deep learning- based EEG classification and should be addressed in further studies (Kamrud et. al. 2021). Future research could use Large Language Models to extract more informa- tion from clinical EEG reports, yielding more valuable insights, more precise diagnostic explanations, and additional information about the clinical context. Additionally, inspection of the textual reports emphasised the importance of integrating contextual information, such as medication use or clinical history. As human experts also consider patient-specific information (e.g. age, medication, clinical history) during EEG analysis (Beuchat et al.) 2021 Kane et al.) 2017 Limotai et al. 2020), future research could extend our methods to incorporate contextual information about the patients' age, medication and clinical history, leveraging approaches similar to those explored by Joo et al. [2023) Samak et. al. (2023) and Thhpa et al. (2024).
Moreover, there is abundant room for further progress in determining whether the use of multi-rater amno- tations or fuzzy labels can enhance model performance and more accurately reflect diagnostic uncertainty in general EEG pathology classification. Multi-rater annotations can improve performance for several key reasons. First, consensus annotations from multiple experts can serve as a 'gold standard" to train and evaluate the performance of deep learning approaches in clinical EEG classification (Hogan el al., 2025) and to provide more reliable ground truth than single-expert labelling (Stephansen et al. 2018). Second, high-quality, labelled data is essential for doveloping accurate and reliable deep learning models. The use of labels from multiple expert raters enables the creation of high-quality datasets, which, in turn, allow models to achieve human-expert-level performance across various clinical tasks (Ge et. al., 2021). Multiple raters can be used to estimate human-level performance empirically. It can also improve label quality by reducing data noise. A systematic optimisation of labelling quality might even help to overcome the current asymp- totic limits of predictive accuracy observed in general EEG pathology classification (Darvishi-
el al., 2023 Kiessner et al. 2024 Poziomska et al. 2025), and hence represent promising avenues for exploration. Moreover, imperfect or ambiguuus annotations from a diverse group of experts can be effectively used to train deep learning models. For example, [Saab et al. (2020) have shown that combining various ratings, even if individually imperfect, can improve seizure detection performance. Furthermore, multi-rater anno- tations reflect diagnostic uncertainty. Annotations from multiple raters can be used to quantify inter-rater agreement (Beuchat et al., 2021), ; thereby implicitly quantifying the degree of diagnostic uncertainty among human experts, against which deep learning models can be compared. Reflecting diagnostic uncertainty is also essential for clinical adoption. Understanding inter-rater variability provides a benchmark for evaluating the performance of deep learning approaches relative to the range of human expert opinions (Hogan et al. 2025). For example, model performance can be compared with human inter-rater agreement to measure the impact of replacing expert with deep learning models on overall agreement (Hogan et al. 2025). The potential of deep learning- based approaches for clinical support is evident when the models align with expert ratings Tjeepkema-Cloosternee
et. al. 2025)
Lastly, with multi-rater annotations, fuzzy labels and fuzzy logic systems can be used for model training and evaluation. These systems provide probabilistic rather than binary classifications of pathology, introducing ambiguity and uncertainty into E.E. interpretations. The use of fuzzy labels can improve model robustness and performance, particularly in challenging scenarios. Fuzzy logic systems are well-suited for handling the noise, nonlinearity, and high variability inherent in EEG signals (Ahmadieh et al. 2025 Sorkhi et al., 2022). Also, fuzzy labels can lead to enbanced feature extraction and classification, resulting in superior accuracy,
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precision, recall, and F-scores compared to single-rater label models (Gu & Cao 2020 Shocibi et al. 2022
For example, fuway-logic-based classification algorithms have achieved improved performance and specificity in classifying Alzheimer's disease, Mild Cognitive Impairment, and healthy subjects (Amezquita- SSachezz
ct al. 2021). Furthermore, fuzzy labels better reflect diagnostic uncertaintics. The use of fuzzy logic systems enables the representation of partial truths and degrees of membership in categories (Hu et al.] 2021) making them an ideal framework for capturing the inherent ambiguities and variabilities EEG analysis. In addition, they can be used to quantify uncertainly, which can be incorporated into models to handle it (Ahmadieh et al., 2025). This enables greater tolerance to noisy conditions and addresses uncertainty arising from the high variability of EEG patterns over time (Sorkhi et al.) 2022). Recently, there has been increased interest in using uncertainty quantification in deep learning-based methods for medical applications to address diagnostic uncertainly (de Jong el al. 2025). For example, converting classification tasks to probabilistic predictions using fuzzy labels for ambiguous samples (Xu et al.] 2024) can help to identify when a model is less specific, guiding clinicians on which samples might require additional manual review (Kang ct al. (021). The fact that deep learning models cannot currently represent predictive uncertainly is recognised challenge (Prince et al. 2023) that warrants further investigation. In summary, previous studies indicate that the use of multi-rater annotations and fuzzy labels can significantly improve model performance and better reflect diagnostic uncertainty in EEG interpretation. By providing richer, more robust training data, multi-rater annotations can help to improve model performance. In addition, multi-rater annotations are essential for evaluating models against human-level performance and for understanding the inherent variability in human EEG analysis. Overall, multi-rater annotations and fuzzy labels are valuable strategies that help build more robust, expert-aligned models by capturing the full scope of human interpretations and intrinsically modelling the ambiguity and inherent uncertainty in EEG signals, leading to more nuanced predictions and potentially enhanced accuracy, especially in clinically challenging scenarios. Together, these approaches contribute to the creation of more reliable and clinically relevant deep learning- based systems for clinical EEG analysis. A natural progression of this work is therefore to integrate consensus labels or probabilistic (fuzzy) targets, which could mitigate label noise issues, improve model performance, and better reflect diagnostic uncortainty in EEG interpretation.
More broadly, research is also needed to validate deep learning- based EEG classification systems in real-world clinical scttings. While large, heterogencous public datasets such as the TUEG are valuable source for the initial development and evaluation of deep learning methods for general EEG pathology classification, their translation into impactful climical (support) systems requires thorough external and real-world validation
[El Arab et al. 2025). Pilot studies and validation in real-world clinical settings with clinician feedback are essential before these systems can be reliably applied in everyday clinical practice (El Arab et al.) 2025
Hosny et al.
2022 Jacobson & Krupinski 2021). One way to do this is through pilot studies, which assess the methods utility, safety, and effectiveness in supporting clinical decision-making and improving patient care. This step ensures practical utility (Zajac et al., 2024), clinician usability (McNamara et al., 2024
Olaoye et al. 2024 effectiveness, robustness to EEG variability and other factors (Kelly et al. 2019 and
fosters trust Beger 2025 Olaoye et al. 2024 Zajac et al., 2024). However, deploying deep lcarning- based methods in clinical practice requires careful consideration of potential risks and challenges. One is the risk of automation bias, where clinicians may rely on model predictions, potentially leading to missed pathology if models are deployed without calibrated thresholds and robust human- comaa--n--the-lee in-the--otho-opp guardrails (McLaren ct al. 2025; Wang et al., 2220 West et al., 2023). To mitigate these risks, the interpretability of the models is essential, as it enables the explanation of predictions and thereby supports clinical judgement. enhances clinician performance, and is particularly valuable in ambiguous cases (Hasan et al. 2025 Mansilla
ct al. 2024). Another risk is the institutional bias inherent in models trained on a databasc from a single institution, which limits their applicability beyond that institutional site and may lead to demographic performance disparities {Bomatter & Gouk 2025 Darvishi-Bayazi et al. 2023 Khan et al. 2022 Saab
et al. 2020 Tjepkema-Cloone Cloostermans et al. 2025).
Further studies are needed to elucidate the classification performance and applicability of models across diverse, multi-institational datasets. Addressing these risks and biases requires data governance and handling protocols. This includes prioritising ethical data sharing, "findable, accessible, interoperable and reusable" (FAIR) principles, and standardised data organisation (Ahluwalia) 2021 Chiang et al.) 2021 Ienca et al.
2022
Ochang et al. 2022 Reer et al. 2023). Another clinical challenge is mitigating model drift over
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time. For example, changes in clinical practice, data-collection equipment, or demographic changes in the patient population can progressively degrade model performance (Aguilar et al.] 2024; Chen et al. 2022:
Davis et al.) 2022 Gonzalez-Gonzall et al.] 2022 Vela et al., 2022, Vishwanath et al. 2023). Strategies such as evaluating model robustness under realistic distribution shifts, continuous learning, and proactive detection of detrimental data shifts are vital for maintaining the reliability and validity of deep learning-
based clinical EEG analysis systems (Coccon et al., 2024; Feng et al., 2022: Gonzalez- et al. 2022;
Guan et al., 2025). Therefore, future clinical implementation must prioritise transparent reporting of model limitations, establish clear protocols of human oversight, and develop adaptive systems that continually learn and remain robust. to inherent variability and biases in real-world clinical data (AbuAlrob et al.) 2025
Mourid et al. 2025). Nevertheless, real-world application testing is pivotal for validating deep learning methods and is an essential step for translation into everyday clinical practice. Future research should
focus on developing methods for seamless clinical integration (Eunab & Mcheick 2024 Zajac et al.
2024
optimising user-interface designs (McNamara ct al., 2024 Olaoye et al.) [024 Zajac et al. 2024), exploring
adaptive learning strategies for continuous improvement. against 'long-tail' cases (Choudhury Asan 2020:
Feng et al., 2022) and establishing comprehensive framework for ongoing performance monitoring and
trustworthiness evaluation across diverse real-world settings /Choudhury & Asan 2020 Feng et al.
2022
Sacnz et al. 2024
Author Contributions
Ann-Kathrin Kicssner: Software, Writing- - original draft, Writing - review & editing, Visualisation, Concep- fualisation, Investigation. Tonio Ball: Supervision. Joschka Boedecker: Supervision, Project administration, Resources.
Acknowledgments
This work is part of BrainLinks-Brain Tools which is funded by the Federal Ministry of Economics, Science and Arts of werdte-errt within the sustainability program for projects of the excellence initiative I1. Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) 417962828. Funded by the Deutsche Forschungsyemeinschaalt (DFG, German Research Foundation) 417962828. We acknowledge funding by the Deutsche Forschungsgemeinschatt (DFG, German Research Foundation) under SFB 1597 (SmallData), grant number 499552394 and under AT-Cog-BA 4695/4-1. This work was funded by BMBF Grants: KIDELIR- 16SV8864 and DiaQNOS-13N16460.
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A Appendix
A.1 Related Work
In this section, we brielly summarise the related work on deep learning methods for general EEG pathology classification that have been successfully applied to the TUH Abnormal EEG Corpus (TUAB)
(Óópez de
Diego 2017 or TUH Abnormal Expansion Balanced EEG Corpus (TUABEXB) (Kiessner et al. 2023 (scc
Table and Table 4 in Section 3.5 In recent years, substantial research has explored various approaches to
general EEG pathology classification, reporting accuracies within a narrow range of 79% to 87%. However,
the majority of previous work is based on the TUAB (see Table 3 in Section 3.5). In this regard, SShhirmmii-- ter et al. (2017a) introduced and applied two CNNs, Deep4Net and ShallowNet, originally proposed for motor imagery and adapted for EFG pathology classification, achieving accuracies of 85.40% and 84.50%, respectively. These model have been successfully reused or reimplemeted in other studics (Darvishi-Bayaz)
et al. 2023 Gemein et al. 2020 Khan et al. 2022 Kiessner et al., 2023; 2024 Van Leeuwen al.) 2019
Western et al. 2021 Wu et al 2021 Since then, several different architectures have been used to classify general EEG pathology. For instance, Gemein et al. (2020) employed two additional networks: compact CNN called EEGNet and a temporal convolutional neural network (TCN). The EEGNet has been proposed by (Lawhern et al. 2018) for BEG-based BCTs using depthwise and separable convolutions. The TCN was introduced by Bai et al., 2018) for sequence modelling and has since been optimised for EEG classification via a neural architecture search (Chrabaszez, 2018 Gemein et al., (2220). Other studics have employed dif- ferent CNNs, such as 1-D CNNs
[Shukla el al. 2021 Yildrrmn et al., (020), or hybrid models that combine 2019a), an Inception- Residual CNN (Wn et al. 2021). or Xception Time Recently, few approaches have explored the potential of a transformer-basee foundation model for EEG pathology classification. The Large Brain Model (LaBraM) model (Jiang et al., 2024) (369M parameters) and the Biosignal Transformer (BIOT) model (Yang et al. 2023) (3.2M parame- ters) were pre-trained on a combination of different datasets using a cross-dataset training and evaluated on the TUAB dataset. Moreover, Roy et al.] (2019a) proposed a deep 1D convolutional gated recurrent neural network, called ChronoNet, with exponentially varying filter sizes in the ConviD 1D layers. While ChronoNet achieved one of the highest accuracies (86.57%), its open-source re-implementation achieved lower perfor- mance (81%) (Khan et al.) 2022). Similarly, despite reusing the original implementation of some of these CNNs, studies reported slightly different results from the original report. The reasons for these differences include variations in EEG preprocessing, such as other input lengths, different versions of the TUAB, or different datascts, as well as differences in hardware and training strategics. On the TUAB, Wu et al. (2021) reported the highest accuracy of 87.10% using the IRCNN model. Overall, the reported approaches achieved a mean specificity of above 84% and a mean sensitivity of about 75-80% on the TUAB. Most models show lower sensitivity than specificity, indicating that they are more likely to classify pathological examples non-pathological than vice versa. Baseline results the TUABEXB dataset have been reported using Deep4Net, ShallowNet, TCN and E.RGNet (see Table 4 in Section 3.5). These four CNNs achieved typical EEG pathology classification accuracies on TUABEXB, with slightly lower accuracies ranging from approx-
CNN and RNN (Roy et al.
2018
model [Brenner et al.) 2024
*rre available in Braindecode, deep learning toolbox for EEG, which available for download https://gsthub.ccm NrT.Freeiburr//briineecoom
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imately 82% to 86%. Consistent with previous studies the TUAB, the models mostly achieved lower sensitivity (72-2%) than specificity (83-96%) on the TUABEXB.
Although multi-branch, multi-scale, or parallel architectures (Altuwaijri et al.) 2022 Belwafi et al. 2017
Ingolfsson et al. 2020 Jia et al.] 2021 Riyad et al., 2020 Szegedy et al. 2015 Zhang et al.] (0032) are now
beginning to gain traction in various
FEG classification tasks, including motor imagery (Altuwaijii et al.
2022 Cai et al. 2024 Jia al.
2021 Liu & Yang 2021 Liu et al. 2023 Yang et al. (021], emotion
recognition [Emsawas et al., 2022
Yan et al.] 2025), , and neonatal sleep staging (Siddiqa et al. 2024 Zhu
et al. 2023 only a few attempts have been made to assess the efficacy of using CNNs with a set of three convolution scales for general EEG pathology classification. For example, |Roy et al. (2019a) has considered the importance of convolutional layers with multiple filters of exponentially varying sizes (2, 4, and 8) to extract and combine features from different time scales. Inspired by the core idea of inception (Szegedy) et al. 2015; Zhang et al.) 2023a), Wu et al. (202T) employed stacked convolution kernels of different, yet small, scales (1, 3, and 5) in parallel. Rocently, Brenner ct al.] (2024) employed an XceptionTime model, in which each module applies a set of three dept.wiss separable convolutions with different kernel sizes (11, 21 and 41). These studies have reported performance improvements, with accuracies ranging from 85.10% to 87.10%. However, these results were basod on the TUAB, and no attempts have been made to evaluate the performance of multi-scale CNNs on a larger, more heterogeneous dataset. In addition, smaller kernel sizes were preferred due to lower computational costs (Emsawas et al., 2022), which, however, tend to extract shorter temporal patterns from faster frequency bands (Cohen 2014 Jia et al.] 2021). In contrast, larger kernel sizes require higher computational costs, while they can learn long-term temporal patterns from slow frequency bands (Cohen 2014 Jía et al., 2021 To date, research has not yet determined whether using raw EEG signals input for multi-branch CNN with multi-scale convolutions, which combine both long-term and short-term temporal patterns by employing kernel sizes based on clinically relevant frequency bands, is effective for classifying general EEG pathology on larger, heterogeneous datasets.
A.2 Additional Details about the Datasets
In this section, we provide additional details on the TUH Abnormal EEG Corpus TUAB 3 López de Diego 2017) and the TUH Abnormal Expansion Balanced EEG Corpus (TUABEXB 6 Kiessner et al. 2023) that we used in our experiments. The two datasets are publicly available subsets of the Temple University Hospital EEG Corpus (TUEG (Obeid & Picone| 2016) for general EEG pathology classification, consisting of EEG recordings labelled as non-pathological or pathological. The TUEG comprises 69,582 clinical EEG recordings from 26,873 EEG sessions involving 15,001 patients over 15 years. Details on the number of TUAB recordings and patients are shown in Table g while Table I0 gives details of the number of recordings and patients in the TUABEXB datasot. For training, we concatenated the TUAB and TUABEXB training sets, which we refer to as the TUH Abnormal Combined EEG Corpus (TUABCOMB). We evaluated our models the predefined test sets of the TUAB and TUABEXB datasets, respectively (see Section 3.3). Details on the number of recordings and patients of the TUABCOMB are shown in Table [1 There is no overlap of patients between the training set of TUABCOMB and the evaluation sets of TUAB and TUABEXB.
A.3 Design Choices and Hyperparameter Optimisation
For our proposed Multi-BK-Net architecture described in Section 2.1 we evaluated several design choices, including architecture hyperparameters, such as total number of temporal filters, filter length of the later convolution layer, strides and types of non-lincarities, as well as algorithm hyperparameters, such as learn- ing rate, weight decay and number of training epochs. In addition, we evaluated potential performance improvements by using intermediate normalisation by batch normalisation (Toffe & Szegedy 2015) group
normalisation (Wu& & He. 2018) as well as the use of exponential linear units (ELU, f(x) ()) = x for x 0 and
f(x) = e -1 f x 1
(Clevert et al. 2016) or GELU (GELU(z) = xP < x) = xx)))) Hendrycks &
$v2.0.0: available for download hwtphh:ppp//wwttt/ issp.
wwtp:iipppppcccpesss...e..s piconepress....omm..... cowpprojjctss/uuu seg/html/doomloae .shmm
TThe EEG recordings part of the TUH EEG Corpus and are publicly available alter registration the TUH EEC Corpus
website praipppiconepees com/projjcts//uull http://hww.eeggea/////oooo/ooo/o
shtml The corresponding pathology labels are available
download github. com/ Com//AKiessnee//UHbnnooooe Exw.rmmiinm----aiiiiii----ooeeee dataset
1.. and v1.2.0; available for download at htwppaalhhiihtppp///pppppapappee
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Table 9: Number of recordings and patients in the TUAB dataset. For 54 patients in the training set, both non- con-poooolo and pathological recordings are available. However, there is no overlap between patients in the training and evaluation sets. For more details on the dataset, see López de Diego (0017) and Obeid & Picone (0016.
TUH Abnormal
Training set
Evaluation set
EEG Corpus (TUAB)
Recordings Patients
Recordings
Patients
Non-pathological
1371
1237
150
148
Pathological
1,346
893
126
105
Total
2,717
2,130
276
253
Table 10: Number of recordings and patients in the TUABEXB dataset. There are 183 patients with both pathological and non-pathological recordings in the training set. The training and evaluation sets do not share recordings from the same patient. Details of the dataset and the labelling procedure can be found in Kiossner et al. (2023)
TUH Abnormal Expansion
Training set
Evaluation set
Balanced EEG Corpus (TUABEXB)
Recordings
Patients
Recordings
Patients
Non-pathological Pathological
4,015
3,253
447
392
3,975
3,166
442
378
Total
7,990
6,419
889
770
Gimpel 2023 activation function. The hyperparameters used and their possible values, which construct the search space, are listed in Table 12
To identify the optimal set of the CNN hyperparameters (Table 12 we employed the automatic hyperpa- rameter optimisation framework Optuna (Akiba et al. 2019), as it has been succossfully used to efficiently
tune the hyperparameters of CNNs (Al-Ja'afrehl et al. 2023 Hanifi et al. 2024 Latreche et al. 2025 Shekhar
et al.) 2021). Optuna implements Sequential Model- Based Optimisation (SMBO) to efficienlly search for the optimal hyperparameters by building probabilistic model of the objective function (Akiba et al. 2019:
Bergstra et al., 2011 Lemos et al., 2021). To scarch for the hyperparametor space and maximise the objective function, we used the Tree-structured Parzen Estimator (TPE) algorithm (Bergstra et al. 2011 2013 We used multivariate TPE rather than independent TPE because multivariate TPE is reported to outperform independent TPE by finding better solutions faster and by better handling problems where there is an inter- action between variables. The optimisation approach is described in detail in Bergst.ra et al. (2011: 2013
Falkner et al. (2018) and Kenny et al.) (2024). In medical diagnosis, identifying all potentially pathological EEG recordings is more important than reducing false- posit-vostiive results. Therefore, two objective measures validation accuracy and validation sensitivity were considered in this study. The multi-objectivee function for hyperparameter optimisation is defined as the maximisatio ion of the mean validation accuracy and mean validation sensitivity using 5-fold cross-validation. For each set of hyperparameters, we performed 5-fold cross- vols--vlidotion on the TUABCOMB training data using StratiffedGGruupPPool from the Scikit-learn library [Pedregosa et al. 2011), with the constraint that patients were non-overlapping across splits (80% for training and 20% for validation). The splits were made at the patient level to avoid using recordings from the same patient in different folds. We additionally shuffled the data before splitting to ensure that splits were not chronologically ordered, similar to the predefined training and evaluation sets in the TUAB and TUABEXB datasets. Experiments were conducted with time budget of 45 hours per fold for each configuration run on a single fold. Runs that exceeded the time limit were pruned after completing the first fold. Runs that crashed (e.g., network configurations that did not fit. in GPU memory) were also pruned. In addition, runs with a validation accuracy or sensitivity below 75% in the first fold were pruned to speed up optimisation. We ran the optimisation process until 100 trials were completed, jointly optimising parameters and architoc- ture layouts. For the top 10 trials, we repeated the 5-lold vross-validttion ten times and computed the mean validation accuracy and mean validation sensitivity, averaging across folds and then across runs. Finally, the
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Table 11: Number of recordings and patients in the TUABCOMB training set dataset. There are 237 patients with both pathological and nonpaathologica recordings. The training set of the TUABCOMB and the evaluation sets of the TUAB and TUABEXB do not share the same patients.
TUABCOMB
Training set
Recordings
Patients
Non-pat non-pathological
5,386
4,490
Pathological 5,321
4,009
Total
10,707
8,549
Table 12: Configuration spaces considered in the search for the Multi-BK-Net architecture. We sampled until 100 configuration trials were completed find the best setting. Learning rate and weight decay were defined trial.suggest float(lo=-True)..
Parameter
Config. Space
Total number of temporal conv filters
Normalisation
[batch, group] [ELU, GELUJ [mean, max] [mean, max]
Activation functions
Pooling mode first block
Pooling mode other blocks
Forth conv-pooling- block
[True, False] [True, False] 0.4 0.6]
Forth conv-pooling-blot bolockng-bookk broader Dropout
Filter length conv blocks
[10115200]
Input window size Weighted loss factor pathological
6000
[1,2,3.4]
Optimiser
[AdamW]
Optimiser betal
[0.5,0.9]
Learning rale
[le-5 le-1
Weight decay
5 le
Batch size
[6,32,64
Number of epochs
[30 - 105]
Number of channels
21
best model configuration, with the highest mean accuracy and mean sensitivity, was used to train the model on the full TUABCOMB training set and then evaluated on the unseen, predefined evaluation sets from the TUAB and TUABEXB (sec Section 3.3 for more details). The final hyperparameters are listed in Table 1 in Section 2.1
A.4 Additional Details on the Baseline Architectures
In this section, we provide additional details on the baseline architectures, the list of hyperparameters used for training, and the training procodure. We additionally included five architectures implemented Braindecode
as baseline: Deep4Net (Schirrmeister et al. 2017b a), Shallow:Net (Schirrmeister et al.) 2017b a), TCN (Bai
et al. 2018 Chrabaszez,
2018 Gemein al. 2020 EEGNet Gemein et al. (2020): Lawhern et al. (2018)
and ChronoNet Roy et al. (2019a). These architectures have been utilised in several studies, demonstrating high performance on the TUAB and TUABEXB datascts across various versions and different preprocessing steps. The architectures are shown in Table 14 and Table 15 For a more detailed explanation of each architecture and its optimised hyperparameters, please refer to the corresponding studies. To ensure fairness in evaluation, we apply the same data preprocessing steps and training stratogy to all architectures. While we have employed trial-wise training strategy in this work, previous studies have mainly used a cropped Schirrmeister et al., 2017a). To adapt the
training strategy (Gemein et al. 2020 Kiessner al. 2023
2024
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Table 13: Optimised hyperparameterss of the four single-scale CNNs that we used as a baseline (Gemein
et al.) 2020 Schirrmeister et al., 2017a Kiessner al. 2023 Chrabaszez 2018). No: Number of. For
Deep ShallowNet and EEGNet, the parameter final conv length was set to "auto' to enable trial-wise training. For TCN, an Adaptive.AgPPoolld layer was added to the final block.
Hyperparameter
Deep4Net
ShallowNet
TCN
EEGNet
No. input channels
21
21
21
21
Input time length
6000
6000
6000
6000
No. start filters
25
40
n.a.
n.a.
No. filters
n.a.
n.a.
55
n.a.
F1 (temporal filter)
n.a.
n.a.
n.a.
8
D depth multiplier
n.a.
n.a.
11.8.
2
F2 (pointwise filter)
n.a.
n.a.
16
Final conv. length Channel factor
67
394
n.a.
187
2
n.a.
n.a.
n.a.
Stride before pool
True
n.a.
n.a.
11a8.
Initial learning rate
0.01
0.000625
0.00112610499112244118
0,001
Weight decay
0.0005
5.8373305376733566--07
Dropout
0.5
0.5
0.05270154233155555
0.25
1.2 decay
n.a.
n.a.
1. 1.7491660095005611
n1a.
Gradient clip
n.a.
n.a.
0.25
nla..
Batch size
64
64
64
64
No. epochs
35
35
35
35
Total No. parameters
303,452
66,242
456,502
7,426
architectures to our framework, we used their trial-wise training approach (Schirrmeister et al. 2017b). All models were trained on the TUABCOMB training set for 35 epochs using the trial-wise training strategy (Schirrmeister et. al.] 2017b). For all models, batch size of 64 was used. The model parameters of Deep4 Net. ShallowNet, TCN, and EEGNet were optimised using the AdamW optimiser @Loshchilov & Hutter 2017). For more details on the hyperparameters used for training, see Table [3] For optimisation of the ChronoNet, we use the Adam optimiser (Kingma & Ba 2017) with a learning rate of 0.001 and the binary cross- entropy loss. The learning rates for the gradient and weight decay updates were scheduled using cosine annealing (Loshchilov & Hutter 2016), and we refrained from learning rate restarts.
A.5 Additional Details on the Evaluation Metrics
In this section, we provide additional details on the evaluation metrics and significance tests used in this study. In our experiments, we evaluated the classification performance of our proposed Multi-BK-Net and comparison models using five performance evaluation metrics: accuracy, balanced accuracy, sensitivity, speci- ficity and F2-score. These measures were determined shown in Table 16 As both evaluation sets are slightly imbalanced (see Table g and Table 10 also reported the balanced accuracy (BACC). Since our task is to classify EEG pathology, and thus no EEG recording containing pathological EEG patterns should be classified as non-patholooical, it is crucial to reduce the false-negalive rate. False posilives, on the other hand, can be excluded by clinicians through a more detailed diagnostic process. As minimising false nega- tives the primary concern in this task, we additionally report the F2-score, which prioritises the model's ability to identify pathological EEG recordings (recall) over the model's ability identify positive instances while minimising false positives.
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Table 14: Architecture the four single-scale, baseline CNNs adapted for trial-wise training and as imple-
mented in Braindccode (Gemein et al. 2020; Schirrmeister al. 2017a Kiessner et al. 2023 Chrabaszez,
2018 E: Number of input electrodes. St: Stride.
Dee
ShallowNet
TCN
EEGNet
25x CoCConD (10x1)
ConvDDD (25x1)
55x 15xxCow1D ID (16)
8xConv2D (1x64)
25x 25xCoD E1xE)
40x CD (1xE)
Weight
BatchNorm
BatchNorm Activation (ELU)
BatchNorm
Activation (ReLU)
16xConv2) (Ex1)
Activation (Square)
Dropout2d (0.25)
BatchNorm
MaxPool (3x1)
MeanPool (75x1) St(15x1)
55xConv1D (16)
Activation (ELU)
Activation (Log)
Weight.Nrrn
MeanPool (1x4) St(1 x4)
Dropout (0.5)
Activation (ReLU)
Dropout (0.25)
Dropout2d (0.25)
55xCouv ID (1)
Activation (ReLU)
Dropout (0.5)
2x Conv2D (394x1)
55xCouv ID (16)
16x Conv2D (1x16)
50xConv21 (10x1) St(3x1)
LogSoftmax
Weight
16x 1onv2D (1x1)
BatchNorm Activation (ELU)
Activation (ReLU)
BatchNorm
Dropout2d (0.25)
Activation (ELU)
MaxPool (3x1)
55x ID (16)
MeanPool (1x8) St(1x8)
Weight
Dropout (0.25)
Activation (ReLU)
Dropout2d (0.25)
Dropout (0.5)
55xConv (55x16)
2x Couv2D (1x187)
100x CoDxCD (10x1) St(3)1)
Weight.Norm
LogSoftmax
BatchNorm
Activation (ReLU)
Activation (ELU)
Dropout2d (0.25)
MaxPool (8x 1)
55x 55xCouv (16) Wermht
Activation (ReLU)
Dropout2d (0.25)
Dropout (0.5)
55xConv1D (16)
200xCD (10x1) St(3x1)
WeightNNrrm
BatchNorm Activation (ELU)
Activation (ReLU)
Dropout2d (0.25)
MaxPool (3x1)
55xConv1D 1D (16)
Weight
Activation (ReLU)
Dropout2d (0.25)
2xCoDD (67x1)
55xConv ID (16)
LogSoftmax
Weight
Activation (ReLU)
Dropout2d (0.25)
55xConv1D ID (16)
Weight
Activation (ReLU)
Dropout2d (0.25)
Linear (55,2)
LogSoftmax
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Table 15: Architecture of the baseline architecture ChronoNet as proposed by Roy et al. (2019a). ChronoNet includes multiple filters of exponentially varying longths in the 1D convolutional layers and dense connections within the GRU layers. Arrows represent skip connections. K: kernel size. st: stride. pt padding. h: hidden_ssst size
ChronoNet
32x 32xConv1D st(2.).p(1.)
32 32xConv (C((4))(((44)),, st,)) 32xComvID(((B) (12))_))
Filter Concat
32x (2x st((.).p(1.))
32 x Con)) st((,))
32x (t2))))))
Filter Concat
()))) st(2)).p(1.)
x2x (2XCon1D(((4.) st().))
32x
Filter Concat
GRU(h332)) GRU))(32))
Filter Concat
GRU))(32))
Filter Concat
GRU(h(32))
Linear layer
Total No. parameters
137,233
Performance evaluation metrics. TP is the number of examples that were correctly classified the positive class, TN the number examples that were correctly classified in the negative class, FP is the number of examples that were incorrectly classified in the positive class, FN is the number of examples that were incorrectly classified in the negative class. F2-score: 3 2 to emphasise the need to minimise TP+EN Higher values of the F2-score indicate better
Table 16:
false negatives, and Precision TP+FP recall.
TP and Recall TP
Performance metric
Mathematical formula
(TP +NN
Accuracy
TT+
TP
TN
Balanced accuracy
2 TP+FN FF+FP
TP
Sensitivity
TP+FN
TN
Specificity
TN+FP
TP
F2-score
(1+89)(
TP+FF TF+EN TP
(32
TP-PP/ + TP
TP4FN
A.6
Detailed Results
A.6.1
Performance Comparison to Baseline Approaches
The classification performance of the Multi-BK-Net and all baseline models on the TUAB is shown in Figure On the TUAB evaluation set, Multi-BK-Net achieved mean balance accuracy of 87.36% indicating
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the best classification performance, which was also statistically significantly better than the bascline models (p < 0.001, Mann-Whitng U test). Compared to four of the baseline architectures, our Multi-BK-Ner achieved a slightly lower specificity, which was not statistically significantly different, except for the difference with Deep4Net (p < 0.001, Mann- Mann-Whitney Whitney test). Figure 9 compares the balanced accuracy and sensitivity of our proposed Multi-BK-Na and all baseline architectures on the TUABEXB evaluation set. Again, the Multi-BK-Net outperformed the baseline models. particular, the Multi-BK-Net achieved higher mean balanced accuracy of 86.99% on the dedicated test set, which was also statistically significantly different from the baseline architectures (p < 0.001, Mann- Mann-Whitney U test). Although three of the baseline models achieved slightly higher specificity than Multi-BK-Net (89.73%), only the difference between Multi-BK- Net and Deep4Net was statistically significant (p < 0.01). Conversely, Multi-BK-Net achieved a statistically significantly higher mean specificity than ChronoNet. (p < 0.05, Mann- Shan-Whilney U test).
TUAB
Balanced Accuracy [%]]
90
86
84
82
78
87.36 (a: 0.16)
81.92 (+ 0.58)
84.84 (+. 0.21)
85.37 (a: 0.131
83.47 (d: 0.33)
85.24 (1 0.18)
76
Multi-BK-Net
Deep4Net***
ShallowNet***
TCN
EEGNet***
ChronoNet
100
98
Specificity [%)]
92
90
88
86
91.93 (z 0.45)
96.93 (= 0.64)
92.93 (t 0.54)
93.20 (a: 0.29)
93.53 (z 0.66)
91.67 (z: 0.32)
Multi-BK-Net
Netp
ShallowNet
TCN
EEGNet
ChronoNet
Figure 8: Performance comparison between our proposed Multi-BK-Net and five baseline architectures on the predefined TUAB evaluation set. Each transparent marker represents the performance of a single run, and each larger, bold symbol represents the mean performance score averaged across ten independent runs (n=10). The mean standard error is given in pesentheses. Stars indicate statistically significant differences in performance score between the corresponding baseline architecture and the Multi-BK-Net (Bonferroni- corrected two-sided
U test, < 0.05: *, < 0.01: ** < 0.001: ***).
A.6.2 Performance Comparison with Previous Reported State-of- of-the--rteerre art Deep Learning Approaches
Several studies have employed various end-to-end deep learning methods for general EEG pathology clas- sification (see Section A. In this section, we provide more details on the comparison of our proposed methods to previous work. For the selection of a previously published work on end-to-end deep learning models for general EEG pathology classification, we considered different architectures that have achieved competitive classification performance and match the following criteria: To ensure a fair comparison, the selected models adopt the
minimally preprocessed input from the same set of multiple standard scalp EEG electrodes and are evaluated the predefined TUAB evaluation set evaluation set. Hence, publications that reported cross-validation results (Muhammad et al. 2021
the predefined TUABEXB
Nahmias
& Kontson 2020: Poziomska et al., 2025), used single-channel data as input (Shukla et. al. 2021 Yildırm et al. 2020), or evaluated their methods on data from a nou-publicly available dataset (Kim et al. 2023
Van Leeuwen et al. 2019)), were excluded from our dircct comparison. Table 3 and Table in Section 3.5
summarise the comparisons between our Multi-BK-Net and other methods on the TUAB and TUABEXB datasets, respectively. Perhaps the most clinically relevant finding is that Multi-BK-Net, with sensi-
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TUABEXB
Balanced Accuracy [%]
90
88
86
82
80
78
76
86.99 (+: 0.09)
82.04 (1: 0.95)
84.45 (2 0.17)
84.95 (2 0.11)
83.18 (2 0.17)
84.50 (2: 0.24)
74
Multi-BK-Net
Deep4Net***
ShallowNet***
TCN
EEGNe
ChronaNet**
100
Specificity [%)]
95
90
89.73 (+ 0.27)
97.09 (=t 0.47)
91.03 (+ 0.64)
90.74 (t: 0.24)
89.24 (# 1.21)
88221 (#: 0.34)
Multi-BK-Net
Deep4Net=**
ShallowNet
TCN
EEGNet
ChronoNet
Figure 9: Performance comparison between our proposed Multi-BK-Net and all baseline architectures on the predefined TUABEXB evaluation set. Performance metrics are averaged across ten independent runs (n=10). The mean standard error is given in parentheses. Conventions as in Figure
tivity of 83.10% outperforms all other approaches, reporting mean sensitivity averaged over multiple runs, by at least 3.26% (see Table 3). Note that Brenner et al. (2024) reported sensitivity of 85.70% for the XeeptionTime model. However, the authors used only the first window of each recording as input during evaluation and reported single-run results, which a significant limitation of this approach. Due to the use of stochastic gradient-bases algorithms with randomised weight initialisation, data ordering, and data augmentation during training, each independent training run produces a different network with better worse performance than the average, with variance of approximately 1% to 2% (Picard 2023 Even if the variance between runs is not very large, there is high probability that the performance of single run will be an outlier, much better or much worse than the average (Picard. 2023 As result, this variance can make it challenging to compare training configurations (Bouthillier et al. 2021 Picard 2023). For example, comparing single runs, Multi-BK-Ner achioved a sensitivity 85.72% with accuracics of 88.41% and 87.68%, and a specificity 90.67% and 89.33%, respectively, thus clearly out out-prfonning the Xception' Time model. Nevertheless, reduce stochasticity and improve comparability between training and model configurations, should report the average of evaluation metrics multiple runs (Wightman et al.) 2021). Hence, direct comparison of publications reporting single-run results would be unfair. Furthermore, comparing Table 3 with Table 4 it can also be seen that the mean accuracy of the Multi-BK-Net is slightly better on the TUAB than on the TUABEXB (+0.74%) At the same time, Multi-BK-Net achieved a higher mean sensitivity on TUABEXB than on TUAB (+1.15%). Interestingly, this result is consistent with previous observations Kiessner et al. 2023), assuming that the differences are due systematic differences in age, sex and pathological feature distribution as well different labelling methods (see Kiessner et al. 2023 for more detailed discussion). Furthermore, we observed that Multi-BK-Ne achieved lower sensitivity than specificity both evaluation sets, indicating lower false- positive rate than false-negative rate. This is also
consistent with the results presented in previons literature (Gemein et al. 2020 Kiessner et al. 2023
2024
López de Diego
2017 Schirrmeister et al. 2017a Western et al. 2021
Overall, one of the main findings of our study is that the proposed model achieved mean accuracies 87 87888% However, as noted and discussed in previous work (Engemann al. 20188 Frénay & Verleysen 2014 Gemein et al. 2020 Kiessner al.] 20244] Poziomssa et al.) [2025] [Sun et al.) 2019). there remains the possibility that this accuracy range may be the upper limit due to imperfect inter-rater agreement and the resulting
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label noise, with model performance remaining below a perfect classification score (100%) and saturating around 88%. For classifying EFG. recordings as pathological or non-pathological, previous research has reported inter-rater agreement of 86 88% between two neurologists (Houfek & Ellingson 1959 Rose et al. 1973). At the same time, Beuchat et al. (2021) have found cven lower mean inter-rater agreements of 82- 86% between multiple EEG technologists and neurologists. Unfortunately, the differences in classífication performance cannot be compared with the inter-rater agreement on these datasets used in this study for training and evaluation, as the labels of the TUAB and TUABEXB are mainly based on the impression of a single expert, as stated in the corresponding EEG report (for more details, see Kiessner et al. 2023 López de Diego 2017 Obeid & Picone, 2016). Also, other studies on general EEG classification suffer from the same uncertainty. However, the need to include ratings from multiple experts has been recognised in other EEG classification lasks. For instance, Stephansen et al. (2018) compared the classification performance of their model with the inter-rater agreement among six raters for classifying sleep stages. The authors found that the model's accuracy increased from 76% (one rater) to 87% (consensus of six raters). Therefore, it can be assumed that the deep learning models for general EEG pathology also perform better than a single rater. It would thus be interesting to compare the classification performance of the proposed model with the inter-rater agreement of multiple raters. As the label noise remains the main limiting factor, it will be important to determine whether and to what extent label quality can be improved. For instance, using labels from an ensemble of multiple raters would be a significant step toward improving label quality. Additionally, using averaged ratings (so-called 'fuzzy labels") for training could further enhance model performance, as they represent a probability of pathology, which also helpful in identifying recordings that are ambiguous or more challenging to categorise or contain more confounding patterns. Furthermore, as shown in Beuchat et al. ) , multiple raters tend to achieve higher mean sensitivity (87%) than mean specificity (75%), the opposite of machine learning approaches, which typically achieve higher specificity than sensitivity. Thus, including multiple raters as a baseline for training could increase the sensitivity of the classification model and also might further improve model performance, as was done for sleep stage classification. Moreover, a systematic optimisation of labelling quality might help the models overcome the current asymptotic limits of predictive accuracy (Darvishi- DDarvishi-Bayari el al.) 2023 Kiessner et al. 2024 Poviomska et al. 2025). and hence lead to even better- peatte-perfoming models for general EEG pathology classification.
A.6.3 UMAP Visualisation of Learned Features
To further explain the proposed network, we visualised the learned features of the Multi-BK-Net before the classification layer using the UMAP (MeInnes et al. 2018) method to map high-dimensional fcature vectors into a two-dinensional UMAP space. This allowed the visual inspection of clustering and separation bet pathological and non-pathological classes. For comparison, we also extracted and visualised the feature reprosentations of the Decp4Net, as it is a single-scale CNN that is most similar in architocture to Multi-BK- Net, the TCN, which achieved the second-highed set of three convolutions. A UMAP comparison plot of the visualisation of the feature representation of the last layer before the classification layer is shown in Figure [0] In general, all models can form discernible clusters for both pathological and non-pathological recordings, but with noticeable overlap. This is reflected in reduced classífication performance, which falls within a narrow range of 83% and 88%. As Multi-BK- Net achieves the best classification performance, it also forms distinct, compact clusters for pathological and non-pathological recordings with minimal overlap. Smaller intra-class distances, i.e., tighter clustering, indicate a more consistent representation within each class, while larger inter-class distances, ie., more distinct clusters, indicate that the model has learned features that effectively discriminate between the two classes. Compared to Mulei-BK-Net, the three baseline models tend to form smaller and more dispersed clusters, possibly indicating greater within-class variability less precise representation of the pathological class. For example, Deep4Net forms less compact clusters, resulting in more dispersed clusters with larger intra-class distances. In addition, we can observe that pathological clusters are more dispersed along the component 1 axis, which may indicate greater within-class variability for pathological samples. This further suggests that these baseline models are less consistent in identifying pathological samples and less capable of capturing their heterogeneity. The slightly lower capability to capture the characteristics of pathological samples may also reflect the three baseline models' lower sensitivity (higher false-negative rate) compared to Multi-BK-Net. Additionally, when comparing the datasets (left vs. right column, Figure 0 we observe
classification accuracy, and the ChrouoNet, which uses a
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that all four models exhibit more distinct feature separation and better-defindd clusters on the TUAB dataset than on the TUABEXB dataset. This suggests that the learned features are more discriminative for the TUAB dataset than the TUABEXB dataset. For all models, the distinction between pathological and non-pathological recordings was less pronounced on TUABEXB. Since better feature separation leads to improved classificat ion performance, this may result in better model performance on TUAB than TUABEXB. A possible explanation for this could be that the TUABEXB is more heterogencous compared to the TUAB [Kiessner et al., 2023), and as shown in previous work, this data heterogeneity can make EEG classification more difficult (Kiessner et al. 2023 Poziomska et al. 2025). Finally, the qualitative observations derived from the UMAP visualisations, which highlight the different degrees of feature separation achieved by various models, are consistent with quantitative performance metries, such as accuracy and sensitivity. For instance, the distinct, compact clustering of data points, indicative of strong feature discrimination, observed for the Multi-BK-Net corresponds to higher accuracy and sensitivity scores than those of other models on both datasets. Conversely, the large intra-class distances obscrved in the visualisations of the pathological samples of the three baseline CNNs align with their lower sensitivily (67-81%). Similarly, models with poorer cluster separation in the UMAP visualisations, such as the Deep4Net, show comparable poorer quantitative performance (accuracy of 82-83%). This consistency between qualitative visualisations and quantitative results supports the validity of the UMAP analysis and provides a comprehensive understanding of model performance.
A.6.4 Ablation Experiments
In this section, we provide additional details on the ablation experiments (Section 3.6) and present additional results. Comparod to the five bascline architectures, our proposed Multi-BK-Net is significantly larger (1,038,683 parameters). For example, it is approximately twice the size of TCN (456,502 parameters). three times the size of Deep4Net (303,452 parameters), and eight times the size of ChronoNet (137,233 parameters). The Multi-BK-Net achieved an accuracy improvement of 2-5% over the baseline models, while sensitivity increased by more than 3.5-17.3%. Previous studies have shown that larger models outperform smaller ones (Darvishi-
2023 Howard et al., 2017 Kiessner et al. 2024 Talmor & Berant 2019: Tan & Le 2019: Sun et al. 2017 Zagoruyko & Komodakis 2016). To verify that the superiority of the over the baseline models was not due to its increased model size, we conducted an ablation study. As the Deep4Net has the most similar architecture to our proposed Multi-BBK-Net, we compared the performance of our approach with that of three larger versions of the Deep4Net (sce Table [7]. In particular, we increased the number of temporal filters in the Deep4Net to 35, the same number used in the first wwwoouution-ooolinng block of the Multi-BK-Neet. We refer to this model as Deep4Net35, which comprises 579,182 parameters, approximately 55% of the size of Multi-BK-Net. We have also increased the number of temporal filters in the Deep4Net to 47 (Decp4Net47). This increases the number of trainable parameters to 1,026,482, approximately 99% of the size of the Multi-BK-Net. Finally, we created a variant called Deep4Net48, which contains 48 temporal filters and has 1,069,490 trainable parameters; thus, it is slightly larger than Multi-BK-Net.
et al.
To further validate the efficacy of our multi-temporal kernel branches in the first convolution-pooling block of our proposed Multi-BK-Nee we also trained and evaluated variants of our model in which we removed four of the five branches and increased the nunber of filters in the remaining branch from 7 to 35. Thus, the variant models consist of a single branch with 35 filters and a single temporal kernel length in the first block. The kernel length of the variants is set to either 3, 7, 10, 13, 25 or 200. The Multi-BK-Net uses kernel lengths of 3, 7, 13, 25 and 200, while the Deep4Net uses kernel size of 10. We refer to these variants as STKSBNet (SingleTTemee Hempporal-Kernd Single-Branch Net). For example, STKSBNet3 denotes the model variant with a temporal kernel length of 3. Furthermore, to investigate the effect of different branches, we included an additional variant with five temporal kernels (3, 7, 13, 25, and 200), but used only one branch instcad of five. Thus, the features of the five temporal convolutional layers are concatenated directly before the spatial convolution. We refer to this model variant as MTKSBNet For more details on the size of the models usod, see Table 17 We trained the STKSBNet and MTKSBNet variants as described in Section 2.1 and the Deep4Net variants as described in Section 3.2 and evaluated all
Saalti-Temporal-KKe Kernel Single-Bran Branch Net).
variants described in Section 3.3
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Size of the different architectures used in the ablation experiments. Model size given as the number of trainable parameters.
Table 17:
Architecture
Model size
Deep4Net
303,452
Deep4Net33
579,182
4ee44Net447
1,026,482
Deep4Net48
1,069,490
STKSBNeL3
1,057,632
STKSBNet7
1,057,772
STKSBNet10
1,057,877
STKSBNet13
1,057,982
STKSBNe025
1,058,402
STKSBNet200
1,064,527
MTKSBNet
1,038,683
Multi-BK-Net
1,038,683
As shown in Table 5 in Section 3.6 the performance Deep4Net increases as the number of filters increases. However, only Deop4Net47 obtained statistically significantly higher mcan F2-score and mean sensitivity on the TUABEXB set compared to Deep4Net (p < 0.05, one-sided Mann-Whitney Whitney U test). All other differences between the original Deep4Net. and its larger variants were not statistically significant (p >> 0.05, Mann- Whitney U test). Nevertheless, Multi-BK-Net outperforms all variants of Deep4Net statistically significantly on both datasets (p < 0.01, Mann-Whitnee Whitney U test). This suggests that the performance improvement of Multi-BK-Nel over Deep4Net is not due to increased model width or size.
Moreover, Table in Section 3.6 shows the performance the variant models and the Multi-BK-Net on the TUAB and TUABEXB evaluation sets. can be seen that when four branches and four kernel sizes are removed, there is a significant decrease in the results on both evaluation sets (p < 0.05, Mann-Whitaee Whitney U tost). Using single kernel length, STKSBNets cannot extract accurate features, resulting in lower overall performance than Multi-BK-Net. This highlights the efficacy of using multiple temporal kernel lengths. Furthermore, when comparing the performance of the STKSBNet variants, it is evident that the optimal kernel size varies across datasets. For TUAB, the mean accuracy is highest at a kernel size of 13, whereas the optimal kernel size for TUABEXB is 200. This observation is consistent with previous studics, which report that determining the optimal kernel length for EEG pathology classification challenging due subject and time differences (Jia et al.) 2021). A possible solution to use multiple kernels of different sizes, which also improves classification performance (Altuwaijri et al. 2022 Jia et al. 2021 Furthermore, the results show that the inclusion of multiple temporal kernels of different lengths, but with single branch, improves performance compared to using a single kernel of single length. This shows that various kernels can learn valuable features and improve classification performance, However, as shown in Table 6 incorporating multiple branches within the first block further improves performance on both datasets compared to using only single branch (MTKSBNet). When the four branches are removed but multiple kernel lengths are used (MTKSBNet), the mean F2-score and mean sensitivity docrease (p 0.05, Mann-Whitncy U test). This shows that introducing multiple input branches improves the identification of pathological samples. Overall, this ablation experiment demonstrates that incorporating multiple temporal kernel lengths and multiple branches, as in Multi-BK-Net, is beneficial for classifying EEG pathology and significantly enhances the model's performance.
A.6.5 Overview of Classification Performance in Different Age and Sex Subgroups
For a more in-depth analysis model performance, we report classification performance on subsets defined by age and sex in Table [8 and Table 19 The tables present a detailed performance comparison of the proposed Multi-BK-Net against five baseline architectures across various subgroups of both the TUABEXB and TUAB datasets. Subgroup analyses conducted by stratifying the test. data by age and and
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Table 18: Performance comparison between our proposed Multi-BK-Net and five baseline architectures on different subgroups of the predefined TUAB evaluation set. Performance metrics were computed separately for each subgroup and averaged across 10 independent runs (n=10). Confidence intervals (95% CT) represent
the mcan based on the normal approximation (n=10). ACC: Accuracy [%), SENS: Sensitivity [%], SPEC.:
Specificity [%]. Bonferroni-correcled, two-sided Mann-Whitnee Whitney U test, p < 0.05: *, p 0.01: ***; n010).
0.001:
Group
Architecture
ACC.
[95%CI|
SENS. [95%C1]
SPEC. [95%CI]
age < 25 y. (n=18)
Deep4Net
90.56
[88 23-92.88
74.00 65.63-82.37
96.92 (94
ShallowNet.
94.44
(92.15-9..74
90.00 (3.47796..33
96.15 (92.78-99.53
TCN
93.33
91..6695.51
94.00 (88.01-99.99]
93.08 (91.57-94.58)
EEGNet
92.31
(90.72-93.90
76.00 [70.77-81223
96.19 (93.86-98.52)
ChronoNet
93.89
91.35-96.43
88.00
(79.33-96.67
96.15 (93.64-98.67
Multi-BK-Net
92.78
(89.51-96.04
86.00
77..63994.37
95.38 99..05-98.72
25 y. S age < 60 y. (n=174)
Deep4Net
83.39
(22.9***0822000 82.20-84.58
69.12**
64.71-73.55
92.55
(88.71-96.38
ShallowNet
85.86
95.15-86.58
75.44
[73.42-77, 46]
92.55
(90.35-94.774
TCN
86.32*
(85.88-86.70
71.76
771.05-7..48
95.66
[95.17-96.13]
EEGNet
85.00***
(84.48-85.52
65.29***
(63.3366688)
97.64
[96.95-98.33]
ChronoNet
88.10
87.63-88.58
73.82**
72.79-74.88)
97.26
[96.83-97.70]
Multi-BBK-Net
89.08
88.43389.73
77.65
76.30-78.99)
96.42
[95.51-97.32
60 y. S age (n=84)
Deep4Net
78.69***
[77.24-80.4]]
79.43
(3.16-85.71]
77.42
[68.38-86.46]
ShallowNet
79.64**
[78.42-80.87]
88.30
[86.67-89.94]
64.84*
[59.64-70.04]
TCN
81.19**
[80.43-8.955
84.34**
83.55-85.13)
75.81
(74.11-77.51
EEGNet
80.95
80.10-81.80
76.60
(74.19-79002)
88.39 *
85553399125
ChronoNet
79.64**
78.552-0..7
84.53**
[83.32-85.74]
71.29
[68.55-74.03]
Multi- Mu-tiiBB-Neet
83.93
83.06-84.80)
89.06
87.73-90.38
75.16
72.84-77.48
female (n=148)
Deep4Net
80.74***
79.64-81.85
72.86*
66.42-79.29
86.59
81.43-91.75
ShallowNet
82.03
[11.12-8..9]
80.48
(8.00-82.89)
83.18
80.26-86.09
TCN
84.53***
(84.07-84.99)
76.83*
(7600077.66)
90.24
89.544990.93
EEGNet
84.05***
(83.+***8333-8888 [S..44884777
69.52
(67.46-71.59
94.82*
19..78-95.... 85.77
ChronoNet
84.66
(84.07-85.20
76.83*
(75.87-77788
90.47
89.60-91.34
Multi-BK-Net
87.43
(86.94-87.92)
82.86
81.33-84.38
90.82
89.50-92.15
male
Deep4Net
84.38*
[82.81-85.
1311-85.99 .94]
74.44**
770.3378.76
94.00
89.61-98.39
(n=128)
ShallowNet
87.42
[86.5-88.26
82.38
80.96-83.81
92.31
[89.57-95.0]
TCN
86.02**
(85.35-6666)
79.05**
(8..14-9.995
92.77
(91.76-93.78
EEGNet
84.61 ****
(83855-85377
71.43***
(69.41-73.45)
97.38
196.19-98.58
ChronoNet
87.34
86.59-88.09
80.95
79.82-82.09
93.54*
92. 92.46-944.2
Multi- Bitti-BK-Nett
88.12
87.58-88.67
82.70
81.06-84.33
93.38
92.60-944 60.10774.17
computing the corresponding metrics and confidence intervals for each subgroup. Performance metrics were computed separately for each subgroup and averaged across ten independent runs. Mean performance metrics and mean 95% confidence intervals were then calculated across ten independent runs using the normal approximation (mean + 1.96 x standard error). With minor exceptions (c.g., for patients younger than 25 years at TUAB), Multi-BK-Net achieved higher mean accuracies and sensitivities than the baseline models across all subsets. For instance, in Table 19 the Multi-BK-Net achieves a mean accuracy of 87.51% for the 25 years < age < 60 subgroup and a mean accuracy of 86. 66.82% for the male subgroup. Similarly, in Table 18 the Multi-BK-NNe obtain a mean accuracy of 88.43% for the 25 years S age < 60 subgroup and accuracy of 88.12% for the male subgroup. While other architectures, such as TCN and E.GNeet, occasionally show very high specificity, they often achieve significantly lower sensitivity. The Multi-BK-Net in rast, maintains strong performance across both sensitivity and specificity, indicating its robust ability to identify both pathological and non non-pathological recordings across different subgroups correctly.
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Table 19: Performance comparison between our proposed Multi-BK-Net and five baseline architectures
on different subgroups of the prodefined TUABEXB evaluation set. Performance metrics were computed separately for each subgroup and averaged across 10 independent runs (n=10). Confidence intervals (95%
CI) represent the mean based on the normal approximation (n=10). ACC: Accuracy [%], SENS: Sensitivity
[%], SPEC.: Specificity [%). Bonferroni-correeted,, two-sided Mann-Whitage Whitney U test, 0.05: *, < 0.01:
p 0.001: ***; 1=10).
Group
Architecture
ACC.
[95%CI]
SENS. [95%CI]
SPEC. [95%CI] CI]
age < 25 (n=144)
Deep4Net
81.94**
[50.60-83.29]
49.58** 04. 44.76-54.411
98.13**
996.92-99.88
ShallowNet
81.11**
[80.12-82.10]
66.67**
644.99-6..94
88.33
86.08-90.55
TCN
82.78*
[81.71-83.85]
68. 12 (66.63-9.62)
90.10
88.40-91..
eEGNet
77.88***
(76.71-7900) (76.877776..77
65.56**
(62.73668.38)
84.41
81158-87.22
ChronoNet
76.67***
75.50-77.84
67.50**
(65.65-69335
81.25 ***
79.10-83.41
Multi-Bis BK-Net
85.62
84444486.81
72.29
70.92-73.66)
92.29
90.54-94.05
25 < age < 60 (n=462)
Deep4Net
82,10
50.37-83.83
59.64***
54.93- 64.35
98.65
98.06-99.22
ShallowNet
84.78***
[44.24-85.32
69.64***
(67.6-71.61)
95.94 ***
(95.24-9666
TCN
85.45**
[84.93-85.98]
72.55***
[7.59-73.51]
94.96***
(94.65-95.22
EEGNet
84.22***
(83.6558888
(83.65-84.79)
69.74***
(66.01-7.448
94.89
(92.92-96.88 86
ChronoNet
86.45
866.1-86.778
75.46*
(74.4476.58
94.55 **
93.99-995.18
Multi- Multi-BK-Net
87.51
87.20-87.82
80.00
79.24-80.76
93.05
92.55-93.54
60 age (n=282)
Deep4Net
82.20
(99.8-84.71
78.48*44
174.1582.822
90.95
88.51-93.39
NhallowNet
85.71
[85.35-86.07
88.79**
(87.57-90.00
78.45
76.13-80.77
TCN
85.28**
(84.92-85.65
88.38*
(87.89-88.87
77.98
776.62-79.33
BEGNet
84.26**
[83.51-85.00]
86.97*
[85.64-88.30
77.86
(73.9-881.8
ChronoNet
85.32*
84.56-86.08
89.29**
888.64-9.955
75.95
74.09-77.82
Multi-BK- BK-Net
86.84
86.19-87.45
91.36
90.99-91.74
76.19
74.19-78.19
female (n=467)
Deep4Net
81.11***
79.01-83.21
65.96***
61.21-70.70
97.14***
(96.21-98.07
ShallowNet
83.79***
[3.37784.21] (83.3788888
76.21
74.51-77.911
91.81*
900.55-9.006
TCN
84.58*
(84.17-84.99)
77.54** *4*
766.96-78.12
92.03
91.35-92.770
EEGNet
82.33***
(81.98-82.69
75.62***
773.31-7.944
89.43
87.00-91.80
ChronoNet
84.35**
(83.3-88...
79.75**
799.02-8049
89.21
88.56-89.88
Multi BK-Net
85.95
65.62-86.29
81.96
$1.39-82.53
90.18
89.83-90.52
male (1=422)
Deep4Net
83.25
81.58-84.92
68.22 ** 63.93-72.51
97.05
96.05-988.0
ShallowNet
84.90-85.67
79.90
78.50-81.33
90.23
888.92 88.92-91.55
TCN
85.43*
(84.88-85998
81.09 *** 80.41-81.76
89.41
88.553 088.53990.29
EEGNet
84.19***
683.76-4463)
78.91 *** 76.38-81.44
89.05
(86.67991.4
ChronoNet
84.72***
84.00-85.400
82.03
81.11-82.95
87.18*
86
Multi-BK-Net
88.18
87.78-88.57
86.98
86.44-87.52
89.27
88.34-90.20
A.6.6 Training Time and Computational Requirements
We report training and inference time and memory in Table 20
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Table 20: Mean training and inference times across runs (n=10) in hours: minutes and mean CUDA memory across runs in GB. Training times are for training only, i.e., excluding data loading and pre-processing. These times are only intended give a rough estimate of training and inference times. Inference time basod on 276 recordings of the TUAB evaluation set.
Architecture Training Time
Training Memory (GB) Inference Time Inference Memory (GB)
Deep4Net
1:05
7.8
0:03
0.60
Nht
1:06
4.01
0:05
10.30
TCN
2:08
2.88
0:02
0.51
EEGNet
1:06
2.61
0:02
0.82
ChronoNet
1:04
2.61
0:04
0.14
Multi BK-Net
12:26
1.59
0:02
1.70
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TUAB
TUABEXB
25
20-
Multi-BK BB---BK-MM -Nt Component
Component
Pathological
Pathological
6.0
10.0
12.5
15
20
Component
Component
Normal Patnological
Normal
30
Pathological
Deep4Net Component
Component 2
20
10
-10
Component
Component
homaa Pathological
Component 2
Component
TCN
Pathological
-5
-10
15
-10
-s
10
15
20
Component
Component
15-
Normal Pathological
ChronoNet
Component
10
Component 2
Patholoical
-10-
Component
Component
Figure 10: A comparison of UMAP visualisation of feature representations learned with Multi-BK-Net.. Deep4Net, TCN and ChronoNet for pathological and non-pathological recordings the predefined TUAB (left column) and the TUABEXB (right column) evaluation sets. Rows represent the UMAP visualisation of different models. Pink dots represent the pathological class, while green dots represent the non- non non-pathological class.
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Reviews:
[{"review_id": "dYrHtHMzlR", "reviewer": "Reviewer_ESqg", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors propose a multibranch, multikernel convolutional neural network, named Multi-BK-Net, to classify clinical EEGs as pathological or nonpathological. They designed the model with an initial block containing five parallel temporal kernels, each targeting a specific clinical frequency band from delta to low gamma.\nThe authors trained their model on the TUABCOMB and TUABEXB datasets and evaluated it on the respective test splits. They report accuracy and sensitivity improvements over baseline methods.\nThe authors' study has some limitations. They tested the model only on datasets from a single institution (TUH), which can limit its validity. Some other limitations: missing ROC curves; address potential data leakage from combining the training sets; limited analysis of potential biases due to factors like age or sex.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The authors show that their results outperform baselines on two predefined test sets. They support their claims with multiple seeds and ablation studies that isolate the contribution of their first-block design. They also report the consistency of the interpretability analyses.\nHowever, the authors' conclusions about \"robust generalisation\" may be limited, as they only demonstrated this within the TUH corpora.", "audience_yn": "Yes", "audience_explanation": "The authors position their paper as “machine learning for EEG pathology screening in healthcare”. They offer a pragmatic CNN based setup that improves sensitivity on EEG based pathology detection. The authors highlight the model's design and implementation as a valuable contribution for both machine learning and neuroengineering audiences.", "requested_changes": "Some suggested enhancements:\n1) External Validation: Evaluate the model on at least one external, non-TUH dataset to substantiate claims of generalisation.\n2) Data Leakage: Address if patient or recording overlaps between the training and test datasets for all datasets.\n3) Clinical Metrics: Report ROC/PR curves and AUCs. For metrics comparison please  provide sensitivity at a fixed 95% specificity for clinical relevance (this is a better metric to compare ML models).\n4) Robustness Analysis: Subgroup analyses for age, sex, artifacts, including confidence intervals for each group would be benificial.\n\nWould strengthen the work\n 5) Compute/latency: Report training/inference memory, time, and real-time feasibility.\n 6) Error analysis: Qualitative review of false negatives/positives for EEGs and report alignment with clinical notes.", "broader_impact_concerns": "The authors can include discussing the risk of automation bias and missed pathology if the model is deployed without calibrated thresholds and essential human in the loop guardrails. The paper should acknowledge the potential for institutional bias from single site training and the risk of demographic performance disparities. It should also address clinical challenges such as data governance and handling (of medical datasets) and the mitigation of model drift over time.", "additional_comments": "None", "extra_scores": {}, "extra_text": {}}, {"review_id": "rjghoELkhR", "reviewer": "Reviewer_NCdX", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents Multi-BK-Net, a convolutional neural network designed to classify EEG recordings as pathological or normal by employing multiple kernel sizes that explicitly target different frequency bands. The central idea is to align convolutional kernels with conventional EEG frequency ranges, with the goal of enhancing generalization across heterogeneous signals. Experiments on the TUAB and TUABEXB datasets show modest improvements over baselines. The authors further support their approach with ablation studies and interpretability analyses.\n\n**Strengths**\n\n* The paper is well-written and well-structured, with clear motivation and thorough experimentation.\n* The evaluation is comprehensive: the proposed method is compared against multiple baselines, and several ablation studies are included.\n* The study uses a cross-subject design, ensuring no patient overlap between training and test data.\n\n**Weaknesses**\n\n*Incremental novelty: Using CNNs to capture different frequency bands is not novel. The main contribution lies in tailoring kernel sizes to EEG frequency bands, which is an incremental improvement. While the results are statistically significant, the actual performance gain is modest, about 2% over the baseline and less than 1% compared to single-branch or single-kernel ablations. It remains unclear what clinical value such improvements provide.\n\n* Although TUABEXB is larger and more diverse, both datasets originate from the same institution, raising concerns about cross-institutional generalization.", "claims_and_evidence_yn": "No", "claims_explanation": "The marginal improvement limits the potential impact of this paper to the community.", "audience_yn": "Yes", "audience_explanation": "Maybe. People in the clinical EEG field will read this.", "requested_changes": "Please clarify your statistical test, it it on n=5 repeated runs?", "broader_impact_concerns": "na", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "l4YizPw1sy", "reviewer": "Reviewer_nwjc", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The goal of this paper is to develop a more robust and generalizable deep learning model, Multi-BK-Net, for classifying pathological versus non-pathological EEG recordings by addressing data heterogeneity and intra-/inter-subject variability through a multi-branch, multi-kernel convolutional neural network architecture. \nThe paper introduces Multi-BK-Net, a multi-branch, multi-kernel convolutional neural network tailored for EEG pathology classification. The core innovation lies in the model’s first convolutional block, which contains five parallel branches, each with a distinct temporal kernel size corresponding to clinically relevant EEG frequency bands: delta, theta, alpha, beta, and low gamma. This design enables the network to capture both short- and long-term temporal patterns across a wide spectrum of physiological variability. Each branch performs temporal convolution followed by spatial convolution and mean pooling, with the outputs concatenated and processed through three additional convolutional blocks before final classification. The model was trained using data from two public EEG datasets—TUAB and TUABEXB—combined into a unified training set (TUABCOMB). To improve generalization and mitigate overfitting, training included trial-wise windowing (60 seconds each), group normalization, GELU activations, dropout regularization, and optimization via AdamW with cosine annealing. Hyperparameters were tuned using Optuna with multivariate Tree-structured Parzen Estimators. The evaluation used accuracy, sensitivity, F2-score, and Mann-Whitney U tests with Bonferroni correction to assess statistical significance, with additional interpretability provided through UMAP feature visualization and amplitude gradient analyses.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper evaluates Multi-BK-Net across two EEG datasets (TUAB and TUABEXB), including both a standard benchmark and a more diverse dataset, and compares its performance to five baseline CNNs and multiple published state-of-the-art models. This dual-dataset evaluation strengthens the credibility of its claims and benchmarks. The paper implements robust evaluation practices including 10-run averages, multiple metrics (accuracy, sensitivity, F2-score), Bonferroni-corrected Mann–Whitney U tests, and ablation studies to isolate architectural contributions. This rigor addresses concerns about variance and reproducibility common in deep learning research.\nThe paper goes beyond performance metrics to include UMAP visualizations and amplitude gradient analysis. These interpretability tools demonstrate that the learned representations align with known neurophysiological features of pathological EEGs, thereby increasing the model’s trustworthiness for clinical applications.\n\nThat being said, some points could be improved. First, single experts label the datasets used, and the authors acknowledge that inter-rater variability may cap classification accuracy. However, no attempt is made to integrate consensus labels or probabilistic (fuzzy) targets that could mitigate this issue. Second, while the model performs well on public datasets, the paper does not include validation in a real-world clinical setting (e.g., clinician feedback or pilot deployment), which limits conclusions about practical adoption or usability.", "audience_yn": "Yes", "audience_explanation": "EEG analysis is of interest to a broad audience in the TMLR community.", "requested_changes": "I would suggest the authors to work on the two weaknesses listed above. I also have a question that could help clarify the contribution: \nCan the use of multi-rater annotations or fuzzy labels improve model performance and better reflect diagnostic uncertainty in EEG interpretation?", "broader_impact_concerns": "There is no broader impact, and one may be beneficial since the proposed model will be used for biomedical data analysis.", "additional_comments": "The paper has solid ideas, but the contribution is more associated to the novel application of already existing techniques.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)