CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction
Authors: Pin-Hua Lai
Wei-Chun Yang
Hsiang-Chieh Tsou
Chun-Shu Wei
Venue: Submitted to ICLR 2023 (2023)
Keywords: EEG
Brain-computer interface
EEG artifact removal
convolutional neural network

Abstract: Human electroencephalography (EEG) is a brain monitoring modality that senses cortical neuroelectrophysiological activity in high-temporal resolution. One of the greatest challenges posed in applications of EEG is the unstable signal quality susceptible to inevitable artifacts during recordings. To date, most existing techniques for EEG artifact removal and reconstruction are applicable to offline analysis solely, or require individualized training data to facilitate online reconstruction. We have proposed CLEEGN, a novel convolutional neural network for plug-and-play automatic EEG reconstruction. CLEEGN is based on a subject-independent pre-trained model using existing data and can operate on a new user without any further calibration. The performance of CLEEGN was validated using multiple evaluations including waveform observation, reconstruction error assessment, and decoding accuracy on well-studied labeled datasets. The results of simulated online validation suggest that, even without any calibration, CLEEGN can largely preserve inherent brain activity and outperforms leading online/offline artifact removal methods in the decoding accuracy of reconstructed EEG data. In addition, visualization of model parameters and latent features exhibit the model behavior and reveal explainable insights related to existing knowledge of neuroscience. We foresee pervasive applications of CLEEGN in prospective works of online plug-and-play EEG decoding and analysis.

Under review as a conference paper at ICLR 2023
CLEEGN: A CONVOLUTIONAL NEURAL NETWORK FOR PLUG-AND-PLAY AUTOMATIC EEG RECON- STRUCTION
Anonymous authors Paper under double-blind review
ABSTRACT
Human electroencephaalorraphy (EEG) is a brain monitoring modality that senses cortical neuroelectrophysioooge
activity in high-temporal resolution. One of the greatest challenges posed in applications of EEG is the unstable signal qual- ity susceptible to inevitable artifacts during recordings. To date, most existing techniques for EEG artifact removal and reconstruction are applicable to offline analysis solely, require individualized training data to facilitate online recon- struction. We have proposed CLEEGN, a novel convolutional neural network for plug-and-play automatic EEG reconstruction. CLEEGN is based on subject- independent pre-trained model using existing data and can operate on a new user without any further calibration. The performance of CLEEGN was validated us- ing multiple evaluations including waveform observation, reconstruction error as- sessment, and decoding accuracy on well-studied labeled datasets. The results of simulated online validation suggest that, even without any calibration, CLEEGN can largely preserve inherent brain activity and outperforms leading online/offline artifact removal methods in the decoding accuracy of reconstructed EEG data. In addition, visualization of model parameters and latent features exhibit the model behavior and reveal explainable insights related to existing knowledge of neuro- science. We foresee pervasive applications of CLEEGN in prospective works online plug-and -lay EEG decoding and analysis.
INTRODUCTION
Since the first record human electroenceehalooram (EEG) performed almost a century ago (in 1924), EEG has been one of the most widely used non-invasive neural modalities that monitors brain activity in high temporal resolution (Koike et al. 2013 Mehta & Parasuraman 2013 Sejnowski et al. 2014). Among variety of modalities, EEG has extensive in the clinical assessment of neurological and psychiatric conditions, as well as in the research of neuroscience, cognitive science, psychology, and brain-computer interfacing (BCI).
EEG signals measure subtle fluctuations of the electrical field driven by local neuroelectrophysio- logical activity of a population of neurons in the brain cortex (Cohen 2017). While the electrodes are placed on the surface of the scalp, undesired artifacts may introduce interruption in the mea- surements and distort the signal of interest. Even in a well-controlled laboratory with well-trained subject who can maximally keep the body still and relaxed, the EEG signals, unfortunately, could be contaminated by inevitable behavioral and physiological artifacts such eye blinks, reflective mus- cle movements, ocular activity, cardiac activity, etc (Croft & Barry 2000, Wallstrom et al.) 2004 Romero et al., (008). practice, difficult to identify and track the sources of artifacts entirely due to their diversity and non-stationarity. Noise cancellation and artifact removal remain major issues in EEG signal processing and decoding.
Currently, numerous methods have been proposed to alleviate the influence of artifacts in EEG signals. Traditional EEG denoising algorithms include filtering, regression, data separation or de- composition (Makeig al. 1995; Islam et al. 2016; Kothe & Jung, 2016). According to previous meta-analyses on EEG artifact removal literature (Ungüen & Garcia Zapirrin 2013 Jiang et al. 2019), independent component analysis (ICA) is especially popular. It is majorly used in 45% of

---

Under review as a conference paper at ICLR 2023
EEG denoising literature. ICA-based artifact removal estimates the component activity by unmix- ing the EEG data in the channel domain. Through manual or automatic identification, one can exclude the artifact components and then reconstruct the EEG data through back projection based on non-artifact components (Jung et al. 2000a). The fast growth of deep learning methods has drawn state-of-the-an performances in variety of machine learning problems (LeCun al.. 2015)- Lately, dcep-learning- base EEG reconstruction has drawn much attention (Leite et al. 2018 Sun
al. 2020 Lopes al., 2021 Lee al. 2020 Chuang et al. 2022). Although these methods can effectively remove artifacts from artificial synthetic signals, their performance in reconstructing real EEG data has not yet been validated in terms of decoding labeled EEG data. Meanwhile, the model design of existing seep-eearningbasee techniques for EEG reconstruction rarely takes the characteristics of EEG into account.
In this work, we propose CLEEGN, Convolational neural network for EEG reconstructioN. CLEEGN is capable of inneeeeeind EEG construction without any training/calibration for new subject. The contributions of this work are three-fold:
a lighh autoencoder CNN. CLEEGN, with subject-independent framework that facilitates plug-and and-play EEG reconstruction.
CLEEGN outperforms leading online/offline methods in providing reconstructed EEG data with the best decoding performance for BCI datasets.
with novel model design dedicated to EEG reconstruction, CLEEGN characterizes pat- terns of EEG interpretable and provides neuroscientific insights.
2 RELATED WORK
Current processing techniques for EEG artifact removal are highly varied based on the context where the algorithm may apply. Earlier attempts of EEG denoising assumed that the EEG signals and arti- facts appear in different frequency ranges. Based on the assumption, some significant artifacts can be eliminated by the linear filtering method during the online stage (Seifzadeh al. (014). Despite the advantages of low computational time, linear filtering hardly removes artifacts that distribute in an overlapped frequency range of EEG signals. Another approach, adaptive filtering (Schlögl al.) 2007]. estimates artifact signals through additional EOG, EMG, ECG channels and removes these noisy signals from the recording signals by regression. Nevertheless, this approach requires addi- tional auxiliary electrodes and raises the cost and inconvenience in practical applications. The blind source separation (BSS) method in EEG denoising was developed by assuming that the recording EEG signals are linear combinations of the signals from noise sources and the brain neurons. One of the most well-known BSS method is independent component analysis (ICA) (Jung et al. 2000a b). which is able to separate EEG signals into independent components (ICs) (Makeig et al. 1995).
Traditionally, the artifact components extracted by ICA are determined and removed through manual inspection. Recently developed ICLabel can label the ICs provenance into seven different categories: brain, eye, heart, muscle, line noise, channel noise, and other (Pion-Tonacie Tonachini et al.) 2019). Artifact subspace reconstruction (ASR) is another automatic approach. which is based the principal com- ponent analysis (PCA) method (Kothe & Jung 2016). The ASR method selects relatively noiseless periods from the multi-channel EEG data as reference based on the data distribution. After project- ing all EEG data to the principa--coppoent domain, high-variance components projected from the artifacts are detected by cutoff parameter The noiseless signals are reconstructed by preserving the components without carrying artifacts and back-projectee to the time domain. The ASR method has been shown capable of improving the quality of ICA decomposition (Chang al. 2020
Recently, neural network-basee methods have been proposed to remove artifacts for EEG data. A variety of network structures have been applied to the framework for removing EEG artifacts and reconstructing clean EEG. A deep convolutional autoencoder (Leite et al. 2018) can enhance the seak-signallto-noise ratio compared to the linear filtering method via a common CNN autoencoder structure, which has been widely used image denoising. Their work shows that it seems practi- cable to transform the EEG waveform through a CNN structure. Later on, a combined framework integrating Bayesian deep learning and ICA (Lee et al., 2020) used thresholding of the EEG data distribution to discard ICs classified as ocular artifacts. These methods leverage the flexibility of deep learning model design and achieve improvements in their assessments. Considering the non-
2

---

Under review as a conference paper at ICLR 2023
N=
Y
BBG-IRCT
Ref. BBG IxCdT
CLEBKEN
Ofline Demoist Methed
Mean Square Eror
CLEEGN Model
Encoder
Decoder
<xIXT -1xOX -IxOT
-NxCO
-N
-DxxXI
Conv2D spoiaal
TotchNoemmD
(t. (0l) tempori
Figure Illustration of the proposed CLEEGN model architecture and the model training flow.
stationary property EEG data and the degradation phenomenon during training, 1D- D-ResCCN (Sun al., 2020) proposed, which adopted an mnception-Resduur module in the network structure. This network is able to remove EOG, ECG, EMG on single-channel synthesis EEG data. Instead of using synthesis EEG data, IC-U-Net (Chuang al. 2022) was created, which generated pairings of noisy and noiseless EEG data through ICA as training data. The proposed neural network is one-dimensional adaptation of U-Net architecture trained with the ensemble of four loss functions to minimize the difference between amplitude, velocity, acceleration, and frequency components from the signals. This work showed that their reconstructed signal has higher SNR and can surely increase the number of brain components classified by ICLabel.
3 MATERIALS AND METHODS
CLEEGN Architecture and Model Training. CLEEGN is designed to map multi-channel noisy EEG into a latent space and reconstruct it into noiseless EEG signals. The architecture CLEEGN shown in Figure The architecture of the encoder inspired by existing CNN model designed for EEG recognition (Wei et al. 2019) and incorporates convolutional blocks that capture spatiotem- poral characteristics of EEG data. The first convolution block is used extract spatial EEG features through convolutional layer containing C' sspttial filters whose shape is (C, 1), where C' is the num- ber of electrodes of the EEG signals. The second convolution block is used extract temporal EEG features through convolutional layer containing Nr temporal filters with shape (1, Lfx x 0.1]),
Table 1: The architecture of CLEEGN.
Block
Layer
#kernels
Size
Output shape
Encoder
Input
C,7,
Conv2D
C
(C,
(,,C,177
Permute
(8,1,C,1)
BatchNorm
Conv2D
NF
(1, Lf./10)
(B, C.
BatchNorm
Decoder
Conv2D
NF
(1, Lf/10)
(B, Nr. C.T)
BatchNorm
Conv2D
C
(C, 1)
(B,
BatchNorm
Conv2D
(C, 1)
1,,1,
C: # channels, T: time points, fs: Sampling rate, Batch size
3

---

Under review as a conference paper at ICLR 2023
Noisy EEG
Reference EEG
Offline Denoise Method
1. Visual Inspection
Reconstructed EEG
2. MSE
sliding window
Pre-trained CLEEGN Model
reconstruct
Figure 2: Schematic of the simulated online processing flow and the assessments of fitness between the reconstructed and reference EEG signals.
where fa denotes the sample frequency of the EEG signals. As for the decoder, we design an approximately symmetric structure the encoder with three convolutional blocks. The first convo- lutional block decodes the EEG feature with Np temporal kernels with shape is (1, Lff x 0.1]). The second block decodes the EEG feature by a convolution layer with C spatial kernels of shape (C, The last convolutional block is for projecting the feature domain back to the original time domain. To maintain the shape between the model inputs and outputs, every convolution block applies zero- padding except the first block in the encoder. The detail of the CLEEGN architecture is available in Table
As illustrated in Figure D. the objective of the proposed method is to minimize the difference be- tween noiscless signals, Y, and the model output, Y. The recording EEG data, X, is the combination of brain signals, Y, and the signals from multiple noise sources, N. CLEEGN can be regarded as a denoising autoencoder that is intended to perform artifact removal EEG data by creating map- ping between multi-channel noisy EEG signals, X, and noiseless signals, Y. The training process of CLEEGN utilizes pairing noisy raw EEG data and noiseless reference BEG data so that the model can learn to transform noisy EEG data into reconstructed EEG data with maximal similarity to the reference data. To generate large-scale and real reference EEG data, we adopted automatic denois- ing methods, ICLabel and ASR, to remove artifacts and reconstruct clean waveforms offline. Rather than synthetic data, the use of real EEG data ensures presence of artifact/noise thus provides realistic evaluation for our model. The pairing EEG recording with C' channels of continuous noisy/noiseless data was further segmented into training sample pairs. The input size is (C, 7), where T is the number of time points based the sampling rate of the EEG data in 4-second segments. The windows size and stride are set as T and 0.5T. Considering the context of plug-and EEG reconstruction, we perform subject-independen training scheme where EEG data into & disjoint sets by subjects. During the training process, one of the sets was left out for testing. Subjects' EEG data in the left-alone set were not involved in both training and validation. A complete experiment on single dataset would result in k different models. The artifact removal performance of model was evaluated by using the left-alone set. The number of subjects in one set and the EEG duration available for each subject depend on the experiment setting and the dataset used.
natural way and
EEG Reconstruction and Evaluation. The reconstructed EEG data of CLEEGN is generated through an online reconstruction simulation. In the online stage, the system performs artifact re- moval on a new subject using a pre-trained CLEEGN model without any requirement of train- ing/calibration. Figure 2 shows the online EEG reconstruction simulated in offline. The EEG data from subject in the left-alone set would be fed into the model sequentially. The stride size of the sliding window is set to 0.5 seconds to minimize the delay in online reconstruction.
The fitness of the model can be evaluated by observing the waveform visually measuring the similarity using the mean square eтror (MSE) between reconstructed and noiseless EEG. Meanwhile, we propose to use the decoding performance of labeled EEG data objective measurement of the reconstructed EEG quality. We employed EEGNet, a compact CNN for end-to-end EEG decoding (Lawhern et al. 2018), as the classifier decode the labeled EEG data in our study.
4

---

Under review as a conference paper at ICLR 2023
Noisy
Noiseless
CLEEGN
Epoch 1
Epoch 10
loss
val Joss
Epuch 25
2000
2003
Figure 4: Reconstructed EEG signals by CLEEGN across training steps.
EXPERIMENTS
Data and Model Fitting. In this work, we applied two labeled EEG datasets, the BCI-ERN dataset f and the MAMEM-SSVEP-I. dataset A We estimate the classification perfor- mance by calculating the score of Area Under the Curve (AUC) in ERN binary classification and Top-1 aceu- racy the SSVEP task. Figure 3 the schematic diagram of the pro- cess of decoding performance esti- mation using the reconstructed EEG data. The segmented EEG epochs are divided into three splits within each subject: training set, validation set, and test set. The ratio between classes remained the same in each set. We perform 20 repeated runs with shuffled data estimate the average performance of each decoding performance evaluation. De- tailed descriptions of the two datasets are available in the appendix at(A.2] Figure(4present, the loss curve and the reconstruction result at the end of three different training iterations in sample run using the ERN dataset. Through this exploration, we can look into the course of model fitting. In the early stage of training, the model reduces the large MSE loss by smoothing the input signals. To further minimize the eттоr between reconstruction and reference (offline denoised signal), the model compensates the intensity better fit the reference signals. At the convergent stage of training, can see the reconstructed waveform is very similar to the one in reference data.
Reconstructed EEG for quality
EEG epoch
Predict Label
True Label
EEG
Accuracy
Figure Objective EEG quality assessment of the recon- structed EEG based on the decoding performance.
Types of Reference Data. As ICLabel, ASR, and the hybrid ASR-ICLabel are employed auto- matically generate large-scale noiseless reference EEG data offline, it is of our interest to investigate what type of reference EEG serves as the best noiseless reference data for the CLEEGN model training. Figure presents the EEG waveforms sampled from Subject 2 in the ERN dataset. The

---

Under review as a conference paper at ICLR 2023
RAW
Reference
CLEEGN
ICA ICLabel
ASR-32
ASR-32-ICA
ASR-16
ASR-I6-CCA
ASR-8
ASR-8-ICA
ASR-4
ASR-4IICA
Figure 5: Visualization raw (gray), reference (red), CLEEGN (blue) EEG waveforms with fline methods by ICLabel, ASR-32, ICLabel, ASR-4, ASR-4-ICLabel. Each segment plots a five-second segment of signals at Fpl, T7, Cz, T8, and 02.
ASR-16 16. ASR-16-ICLan ASR-8, ASR-8-
CLEEGN
Reference
0.750
12
0.725
0.700
10
0.675
0.650
0.625
0.600
0.575
ASR-4
ASR-4-ICA
ASR
ASR ICC
AGR-16
ASA 18-ICA
ASR 32
ASR-32-1EA
KCLabel
0.50
ASR
ASR-4-CA
ABR
ASR-8-CA
ASR 10
ASR 16ICA
ASR 32
ASR ICA
Laaeel
Type reference data
Type reference data
(a)
Figure 6: (a) Overall fitness of the CLEEGN model across types of reference data using the ERN dataset. (b) Decoding performance of the CLEEEGN-reonssruccee EEG data (blue) and the corre- sponding reference data used for CLEEGN model training (red).
EEG time series from top to bottom represent the recording of Fpl, T7, Cz, T8, and 02 channels. The nine sub-figures visualize the comparison between the noisy raw data and the reference data reconstructed by the ICLabel, ASR with or without ICLabel when the cutoff parameter & was set to 8, 16, and 32. We can the waveform of ICLabel and the ASR-ICLabe abel hybrid method with

---

Under review as a conference paper at ICLR 2023
4 different cutoff parameters k are similar, and the noiseless waveform reconstructed by CLEEGN are highly correlated to the result from these 5 methods. High-amplitude ocular artifacts such as blinking and eye saccades are phenomenal in frontal channels (Fp1) and muscular artifacts featuring high-frequency (around 24-30 Hz in the & band) are observed on the T7 channel. CLEEGN, ICA, and ASR-ICLabe LCael hybrid methods can eliminate these kinds of artifacts. Compared to the ICI Label and ASR-ICLabb hybrid methods, ASR is unable to eliminate the high-frequency EMG artifact for this dataset. The tolerance of large amplitude EOG artifact increases with the employment of larger cutoff parameter k. Limited by the reference data generated by ASR, CLEEGN preserves the high-frequency artifact. As for the large amplitude artifacts that failed to be identified by ASR-32 and 16, we can see that CLEEGN is able to mitigate those EOG artifacts. As shown in Figure 6(a), the reference data prepared by ICLabel offer the lowest MSE, i.e. the best fitness for CLEEGN training. We also compare the types of reference EEG data and their corresponding CLEEGN recon- struction results in the decoding performance of the ERN EEG dataset to assess their data quality. As illustrated in Figure bb). CLEEGN reconstruction draws better performances for all denoising methods. Although the performance decreases with a smaller cutoff parameter k, CLEEGN can outperform the reference data used for its training. This result suggests that our method not only removes the artifact but also preserves informative brain activity in the EEG under our cross-subject training scheme. As the ICLabel provides the reference data with the best decoding performance and fitness, we chose ICLabel as the source of reference data for further experiments in this study.
Training Data Size. We explored the effect of data length per subject in the training set regarding the fitness and the decoding performance of CLEEGN. The training data were segmented from the first 1, 2. 10. 20, and 30 minutes of data in each EEG recording to investigate the trade-off be- tween training time and reconstruction performance. Figure 7 a) shows the fitness of CLEEGN to the reference data. The model trained using the first 10 minutes obtain the minimal value among all duration configurations. Though the difference in MSE between 10, 20, and 30 minutes is not noticeable, Figure| (b)) shows that using the 10-minute training data yields the best decoding per- formance among all settings. Interestingly, with only one-minute training data from each subject, CLEEGN can achieve comparable decoding performance to the reference data. This indicates that CLEEGN retains its performance even when each subject only contributes short recording for training. In addition, we explore the effect of the number of subjects included in the training set on the fitness of CLEEGN model training and the decoding performance of the reconstructed EEG data. We randomly reduced the number of subjects for training from 12 to 6, 4, and 2. Since the subset of subjects may influence the performance, tested multiple combinations and averaged
Table Overall performance over all subjects in the BCI-ERN dataset.
Method
MSE I
AUC
Total parameters $
ICLabel (reference)
0.7218
Raw data (noisy)
12.034
0.0070+0.00100
ID-ResCNN
6.7147=0.5025
0.6697
325891
RNN
10.6686:00.257
0.6708
787984
SCNN
0..623
0.6050
16815552
IC-U-Net CLEEGN
5,2086=10.4200
0.6949+0.0140
2683192
3.5984:10.2538
0.725210.0018
220755
Table 3: Overall performance over all subjects in the MAMEM-SSVEP-IT dataset.
Method
MSE 4
Accuracy 1
Total parameters
ICLabel (reference)
0.4940=000066
Raw data (noisy)
1.3692+1.0444
0.2782+0.0058
ID-ResCNN
0.2047+:0.052
0.2908
313735
RNN
0.1095
0.2806
751516
SCNN
0.1002+10.014
0.00594
16038324
IC-U-Net CLEEGN
0.0098
0.3422-000253
2664724
0.0777+0.0011
0.0122
14043
7

---

Under review as a conference paper at ICLR 2023
the results. With the decrease in the number of subjects, the MSE value and the standard error (light span area) increase in Figure aa), which indicates that the generalization ability of the CLEEGN model reduces when fewer subjects included for training. In Figure b), observe slight decrease decoding performance. We consider the number of subjects as an essential factor in the performance of CLEEGN, yet it requires only few subjects achieve satisfactory performance compared to the reference data.
Performance Comparison. We compare the performance of CLEEGN against five baseline methods, ICLabel, ID-ResCNN Sun al. 2020p. IC-U-Net (Chuang et al. 2022) and the simple CNN
CLEEGN
Reference
0.72
4.5
(SCNN) structure and RNN structure proposed in previous work (Zhang ctt al., 2020). Except for the ICLabel that operates offline, CLEEGN and the other neural network-based methods perform simulated online reconstruction based on the same training process with the reference data generated by ICLabel. Results of the ERN EEG dataset are shown in Table 2 CLEEGN has the best fitness to the reference data with the minimum MSE, the highest AUC score in the decoding performance, and the least parameters. For the SSVEP EEG data, although not having the minimum MSE, CLEEGN outperforms other methods in the decoding accuracy and the model size. The evaluation across the two datasets suggests an overall superiority of our proposed CLEEGN model over other existing neural network-based methods in online reconstruction. CLEEGN even provides better reconstruction than the offline ICLabel. These promising results indicate the usability of CLEEGN in online traiiin/caliiraaiion-ree EEG reconstruction that truly meets the need for real-world applications of EEG-based BCI.
4.25
0.70
4.00
3:73
0.57
Dota longth per subject for traicing (ma)
Data length per subvect for truningg (ntin)
(4)
(b)
Figure 7: Performance of CLEEGN against the training data length per subject evaluated by the BCI-ERN dataset (a) the fitness to the reference data; and (b) the decoding per- formance.
Visualization. On account of the in- terpretability of the CLEEGN model, we visualized its intermediate la- tent features by mapping onto 2- dimensional domain based on the principal component analysis (PCA) of the noisy raw EEG data (Wold let al., 1987). Figure (aa) shows the principal component space and noisy EEG data channel projections. We observe that the scatters of noisy EEG data retain the spatial relation- ship of the actual EEG electrodes. The arrangement of frontal electrodes (prefix in Fp. P) to the posterior (pre- fix in P. O) are projected along the x- axis from right to left. The projection from top to bottom matches the left- side electrodes (suffixed in odd num- bers), central electrode (suffixed in z), and right-side electrodes (suffixed in even numbers). The projection suggests that EEG data of adjacent channels tend to show similar waveforms. The blue dots in Figure b),, (C) are the projection of latent features resulting from the first and second con- volutional layers, which make up the encoder design of CLEEGN. The projection of latent features from the next two convolutional layers is presented in Figure Id), (e). From Figure (9b) to (e), the distribution range of the latent features shrink in the encoder design and expand in the decoder design of CLEEGN. Figure f) compares the distribution of noisy data and CLEEGN
CLEEGN
Reference
0.73
0.72
0.71
0.70
0.69
0.88
0.67
0.66
0.65
12
22
Number of subjects for tmaning
Number of subjects for caining
(6)
Figure 8: EEG reconstruction performance of CLEEGN against the number of subjects evaluated under the BCI- ERN dataset by (a) the fitness to the reference data; and (b) the decoding performance.
8

---

Under review as a conference paper at ICLR 2023
Nosy
N:ky
CLNNOO LF
Mayy
Do
9
141
22
12
2
301
Figure 9: PCA visualization of latent features in CLEEGN for a single subject the ERN dataset. (a) The noisy EEG data projected the PCA space of the noisy EEG data. (b)-(e) Latent features in the first to fourth convolutional layers projected on the original noisy PCA space. (f) CLEEGN reconstructed EEG data projected on the original noisy PCA space.
reconstructed data, showing that the CLEEGN reconstructed data is more compact cluster than the noisy EEG in the PCA space. This implies that the process within CLEEGN includes a projection of the original EEG data in the upstream layer, complex temporal filtering and combination in the midstream layers, and a final projection that converts the noiseless latent features back to the channel domain.
5 CONCLUSION
In this work, we have proposed CLEEGN, a novel convolutional neural network for plug-and-play automatic EEG reconstruction. The training of CLEEGN leverages the conventional offline de- noising methods, ASR and ICLabel, with automatic component classification to generate abundant noiseless EEG data. The performance of CLEEGN was objectively validated using multiple eval- uations including waveform observation; reconstruction eтror assessment; and decoding accuracy on well-studied, labeled datasets. The experiment results suggest that, even without any calibra- tion, CLEEGN can predominantly preserve inherent brain activity. According to the decoding per- formance of reconstructed EEG data. CLEEGN outperforms other neural network-based denois- ing methods on both ERN and SSVEP EEG decoding. From the visualization of the waveform, CLEEGN can remove artifacts from different sources and the waveform is highly correlated to the reference data. Through the visualization of model parameters and latent features, we exhibit model behavior and reveal explainable insights related to existing knowledge of neuroscience. CLEEGN effectively learns the transformation of EEG reconstruction from existing technique and even out- performs conventional offline approach. Future extension of this work includes incorporating other EEG denoise methods and their mixed use, and enhancing the inference ability across datasets or even across recording montages. We foresee pervasive applications CLEEGN in prospective works of EEG decoding and analysis.

---

Under review as a conference paper at ICLR 2023
REFERENCES
Chi- Yuan Chang, Sheng-Hsiou Hsu, Luca Pion-Tonachini, and Tzyy-Ping Jung. Evaluation of artifact subspace reconstruction for automatic artifact components removal in multi-channel eeg recordings. IEEE Transactions on Biomedical Engineering, 67(4): ((4::1114-1121) 1121, 2020. doi: 10.1109//ME.2209999111.
Chun-Hsiang Chuang, Kong-Yt Yi Chang, Chin-Sheng Huang, and Tzyy-Ping Jung. Ic-u-net: u- net-based denoising autoencoder using mixtures of independent components for automatic ceg artifact removal. Neurolmage, 263: 19586, 2022. ISSN 1053-8119.
Michael X Cohen. Where does eeg come from and what does it mean? Trends in neurosciences, 40 (4):208-218, 2017.
Rodney J Croft and Robert Barry. Removal of ocular artifact from the eeg: a review. Neurophysi- ologie Clinique/Clinical Neurophysiology, 30(1):5-19, 2000.
Md Kafiul Islam, Amir Rastegarnia, and Zhi Yang. Methods for artifact detection and removal from scalp eeg: A review. Neurophysiologie Clinique/Clmicall Neurophysiology, 46(4):287-305, 2016. ISSN 0987-7053. doi: https://doi.or//100 http://dbi.org/10.1116.....e.m new.//...ecll..2166... 07.002.
Xiao Jiang, Gui-Bin Bian, and Zean Tian. Removal of artifacts from eeg signals: review. Sensors, 19(5):877, 2019.
Tzyy-Ping Jung, Scott Makeig, Colin Humphries, Te-Won Lee, Martin J Mckeown, Vicente Iragui, and Terrence J Sejnowski. Removing electrenncephalograpiic artifacts by blind source separa- tion. Psychophyssolooyy 37(2):163-178 2000a.
Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield, Jeanne Townsend, Eric Courchesne, and Ter- rence J Sejnowski. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clinical Neurophysiology; 111(10):1745 1775511744-7788 2000b.
Shinsuke Koike, Yukika Nishimura, Ryu Takizawa, Noriaki Yahata, and Kiyoto Kasai. Near-infrared spectroscopy in schizophrenia: a possible biomarker for predicting clinical outcome and treatment response. Frontiers in psychiatry, 4:145, 2013.
Christian Andreas Edgar Kothe and Tzyy-Ping Jung. Artifact removal techniques with signal recon- struction, 2016. US Patent 14/895,440.
Vernon J Lawhern, Amelia Solon, Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, and Brent Lance. Eegnet: a compact convolutional neural network for eeg-based brain- computr interfaces. Journal of neural engineering, 15(5)0056013, 2018.
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):3364444 2015.
Sangmin S Lee, Kiwon Lee, and Guiyeom Kang. Eeg artifact removal by bayesian deep learning & ica. In 2020 42nd Anmual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 932-935. IEEE, 2020.
Niago Moreira Nobre Leite, Eanes Torres Pereira, Edmar Candeia Gurjão, and Luciana Ribeiro Veloso. Deep convolutional autoencoder for eeg noise filtering. In 2018 IEEE International Conference Bioinformatics and Biomedicine (BIBM), pp. 2605-2611 IEEE, 2018.
Fábio Lopes, Adriana Leal, Júlio Medeiros, Mauro F. Pinto, António Dourado, Matthias Dümmpllaan, and César Teixeira. Automatic electroenccephalogamm artifact removal using deep convolutional neural networks. IEEE Access, 9:149955 1194499955199990..111 2021.
Scott Makeig, Anthony Bell, Tzyy-Ping Jung, and Terrence Sejnowski. Independent component analysis of electreencephalograpiic data. In D. Touretzky, M.C. Mozer, and M. Hasselmo (eds.), Advances in Neural Information Processing Systems, volume 8. MIT Press, 1995.
Ranjana K Mehta and Raja Parasuraman. Neuroergonomics: a review of applications to physical and cognitive work. Frontiers in human neuroscience, 7:889, 2013.
10

---

Under review as a conference paper at ICLR 2023
Luca Pion- Ton--Tonachii, Ken Kreutz-Delgado, and Scott Makeig. Iclabel: An automated electroen- cephalographic independent component classifier, dataset, and website. Neurolmage, 198:181- 197. 2019.
Sergio Romero, Miguel A Mañanas, and Manel J Barbanoj. A comparative study of automatic tech- niques for ocular artifact reduction in spontaneous eeg signals based on clinical target variables: simulation case. Computers in biology and medicine, 38(3)348 -600 2008
A. Schlögl, C. Keinrath, D. Zimmermann, R. Scherer, R. Leeb, and G. Pfurtscheller. A fully au- tomated correction method of eog artifacts in eeg recordings. Clinical Neurophysiology, 118(1): 98-104, 2007. ISSN 1 288-2457.
Sahar Seifzadeh, Karim Faez, and Mahmood Amiri. Comparison of different linear filter design methods for handling ocular artifacts in brain computer interface system. Journal of Computer & Robotics, 7:51-56, 2014. ISSN 2345-6582.
Terrence J Sejnowski, Patricia S Churchland, and J Anthony Movshon. Putting big data to good use in neuroscience. Nature neuroscience, 17(11);1440, 2014.
Weitong Sun, Yuping Su, Xia Wu, and Xiaojun Wu. A novel end-to-end Id-rescnn model to remove artifact from eeg signals. Nearocomputing, 104:108-121, 2020. ISSN 0925-2312.
Jose Antonio Urigüenn and Begoña Gareia-Zapirain. Feg artifact removal -ffte-of-thh-aat and guidelines. Journal of neural engineering, 12(3):031001, 2015.
Garrick L Wallstrom, Robert E Kass, Anita Miller, Jeffrey F Cohn, and Nathan A Fox. Automatic correction of ocular artifacts the eeg: a comparison of regression-based and component-based methods. International journal of psychophysiology. 53(2):105-119, 2004.
Chun-Shu Wei, Toshiaki Koike-Akino, and Ye Wang. Spatial component-wiss convolutional net- work (scenet) for motor-imagery eeg classification. In 2019 9th International IEEF/EMBS Con- ference on Neural Engineering (NER), pp. 328-331, IEEE, 2019.
Svante Wold, Kim Esbensen, and Paul Geladi. Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3):37-52 1987.
Haoming Zhang, Mingqi Zhao, Chen Wei, Dante Mantini, Zherui Li, and Quanying Liu. Eegde- noisenet: A benchmark dataset for end-to-end deep learning solutions of eeg denoising. 2020.
A APPENDIX
A.1
NEURAL NETWORK TRAINING CONFIGURATION
A.1.1 CLEEGN
For training CLEEGN, we used the Adam optimizer with an initial learning rate of 1e-3 without weight decay. Besides, the exponential learning rate scheduler is applied with a gamma of 0.8. As for the loss function, we used Mean Squared Error (MSE). The batch size set to 64 and the total training epoch is 40. During the training procedure, the model is evaluated using the validation subset at the end of every epoch with the purpose of saving the weights that achieved the lowest validation loss.
A.1.2 IC-U-NET
The optimizer adopted in IC-U-Net (Chuang et al. 2022) training is SGD with an initial learning rate of le-2, momentum of 0.9, and weight decay of 5e-4. The learning rate scheduler used in the training procedure is the multistep scheduler. As for the loss function, novel ensemble loss proposed in IC- U-Net is adopted. This ensemble is a simple linear combination the Mean Squared Error (MSE) in amplitude, velocity, acceleration, and frequency components of EEG signals. Each term in the loss function has same weight. The batch size set to 64 and the total training epoch is 200. The weight saving strategy in IC-U-Net training is the same CLEEGN.
11

---

Under review as a conference paper at ICLR 2023
A.1.3 ID-RESCNN
The Adam optimizer with initial learning rate of le-3 and the Mean Squared Error (MSH) loss function is adopted in ID>ResCNN (Sun et al., (020) training. Since the ID-ResCNN is developed under one-dimensional synthesized EEG data and no explicit instruction of using multi-channel EEG provided, we trained ID-ReSCNN using two different method. For one method, we trained multiple models for each EEG channel respectively. For the other method, we viewed each multi- channel EEG segment as a batch and the channel arrangement within batch is fixed. Under our experiment, the result showed that the second method not only provides an efficient training process, but also results in better reconstructed performance. The same weight saving strategy in CLEEGN adopted in ID-ResCNN training.
A.1.4 EEGDENOISENET (SCNN, RNN)
Two further simple network architecture are adopted in the comparison, the simple convolutional structure and recurrent network structure. Mean Squared Error (MSE) loss function is adopted as the objective criterion. The weight saving strategy in SCNN and RNN training is the same as CLEEGN.
A.1.5 DECODING PERFORMANCE EVALUATION MODEL: EEGNET
EEGNet (Lawhern et al. 2018) is famous EEG decoding model and widely used in EEG literature. In the original EEGNet paper, they investigated their proposed model with a different number of kernels and denoted the model with Fi temporal filters and D spatial filters as EEGNet-Fi.JD We use two different settings to train the two datasets used. In ERN classification, we use the EEGNet- 8,2 structure suggested by the EEGNet paper. As for the SSVEP classification, an experimental result showed that EEGNet-100. can draw the best performance. We trained and evaluated the decoding performance individually for each subject. We divided the collection of event epochs into three splits within each subject: training set, validation set, and test set with a ratio of 3:1:1. The ratio between classes remained the same in each set. The loss function is categorical cross entropy (CCE) and the Adam optimizer is adopted with a learning rate of 10 and zero weight decay. The batch size is set to 32 and the total training epoch is 200.
A.2 DESCRIPTION OF DATASETS
A.2.1 DATASET 1: FEEDBACK ERROR- RELATED NEGATIVITY (ERN)
Error-related negativity (ERN) can categorized as a kind of event-related potential (ERP), which occurs after errоneous abnormal event perceived by the subject. One characteristic of the feed- back ERN is a relatively large negative amplitude approximately 350 ms and a positive amplitude approximately 500 ms after visual feedback triggered by the error event. In this work, mainly use a well-studied EEG dataset from the BCI Challenge competition hosted by Kaggle to evaluate the artifact removal effectiveness. This dataset includes EEG recordings of 26 subjects (16 subjects labeled and 10 subjects unlabeled) that participated in P300 speller task. P300 speller is a well- known BCI system that develops typing application through P300 response evoked potential. The ERN experiment was conducted under the assumption that the ERN occurred if the subject received incorrect prediction (feedback) from the P300 speller. The objective of the competition was to im- prove the P300 speller performance by implementing error correction through ERN potentials. We used the 16 subjects with labeled data of which the sampling rate is 200 Hz. initially with 56 passive Ag/AgCI EEG sensors.
In the interest of increasing the usability of EEG data, we applied some pre-processing procedures to each EEG recording. The EEG data were down-sampled to 128 Hz and re-referenced by the common average reference (CAR) method to eliminate common-mon mode noise and to zero-center the data. Each recording was band-pass filtered to 1-40 Hz through the FIR filter implemented by EEGLAB to remove DC drifting. During EEG decoding evaluation, we epoch EEG signals in [0, 1.25] second interval obtain correct and erroneous feedback.
12

---

Under review as a conference paper at ICLR 2023
A.2.2 DATASET 2: STEADY STATE VISUALLY EVOKED POTENTIAL (SSVEP)
Steady state visually evoked potential (SSVEP) is another kind of ERP that is characterized as peri- odic potential induced by rapidly repetitive visual stimulation. The SSVEP is composed of several discrete frequency components, which consist the fundamental frequency of the visual stimulus as well as its harmonics. To investigate the generalization ability of the model, we use "EEG SSVEP Dataset II" from Multimedia Authoring & Management using your Eyes & Mind (MAMEM). The dataset includes EEG data from 11 subjects and consists of five different frequencies (6.66, 7.50, 8.57, 10.00, and 12.00 Hz). Each subject was recorded in five sessions and each session included 25 trials (5 trials for each class). The data used 256-channel HydroCel Geodesic Sensor Net (HCGSN) and captured the signals with a sampling rate of 250 Hz. Since there are several different bad channels in each subject's EEG recording, we preserved 20 common channels from each sub- ject to train the CLEEGN model. Every recording was down-sampled to 125 Hz. re-referenced by the common average reference (CAR) method, and band pass filtered 1-40 Hz. We epoch EEG signals in [1. 5] second interval for each event recorded timestamp. The first second was discarded under the consideration of a reaction delay of the subject.
A.3 INVESTIGATION OF DECODING PERFORMANCE EVALUATION: SSVEP
In this work, we mainly use decoding performance as the assessment of different artifact removal methods. We provide interpretation of the relationship between EEG quality and decoding per- formance. In Section 4 SSVEP result, IC-U-Net can optimize the weight with minimal MSE value among all compared artifact removal networks. However, the decoding performance is worse than CLEEGN and even the reference method (ICLabel), which implies that the average error over data points (MSE) is not the best assessment of EEG quality.
Klabel
CLEEAN
CLEEOW
I.U Mes
MM
M mmmmam
Frequency (1)
Frequency
Frequency (c)
(b)
Clabel CLEEON 50- RD-RWOOOM
Clabel 30-0wICWW
30
60
10
60
Frequency (d)
Frequency
Figure 10: Power spectrum density of EEG data on different methods to each class
In the SSVEP experiment, the fundamental frequency of the external rapidly repetitive visual stim- ulation and its harmonics can be important feature in classification. Hence, higher quality in the frequency component of EEG data is required. We use the power spectrum density (PSD) to interpret the result of decoding performance. Figure I0 shows the PSD of EEG event data in each class denoised by different methods. We can see the reconstructed EEG spectrum from CLEEGN in each class similar the reference. As for IC-U-Net, it seems that the model can not completely
13

---

Under review as a conference paper at ICLR 2023
reconstruct in several frequency bands (15-20Hz, 30-40Hz), which leads the low decoding per- formance. The PSD result shows that there is great distortion in the power density of reconstructed data generated by ID-ResCNN. Since ID-ResCNN is diedennnnional structure, hypothesize that spatial information is important in REG artifact removal.
A.4 ADDITIONAL TABLES
Method Subject
ICLabel
ASR-3
ASR-32-ICA
MSE
AUC
MSE
AUC
MSE
AUC
S02
3.2835
0.8432
9,4341
0,8102
3.6285
0.8398
S06
2.5723
0.6609
10.7221
0.6234
2.7348
0.6093
S07
4.7804
0.7835
17.2428
0.7155
4.6049
0.6889
S11
7.2317
0.6214
9.2276
0.5939
6.7069
0.6243
S12
3,2090
0.7126
16.5107
0.6789
3.0982
0.6794
S13
4.3666
0.7860
9.7730
0.7937
4.0034
0.7849
S14
2.0056
0.8256
2.6510
0.7951
1.9174
0.8030
S16
6.3664
0.6758
15.2617
0.6437
5.9677
0.6542
S17
4.5280
0.8068
10.1852
0.8080
8.1861
0.8023
S18
4.3724
0.7629
9.2606
0.6835
3.6407
0.7331
S20
4.6405
0.7022
11.5464
0.6898
3.7116
0.6669
S21
5,0383
0.7219
21.3344
0.6827
5.0986
0.6569
S22
4.5625
0.7300
20.2808
0.6334
4.8429
0.6850
S23
3,4311
0.7086
3.8892
0.6991
2.5044
0.7163
S24
3.2064
0.7310
4.7346
0.7297
3.6940
0.7045
S26
5,3556
0,5614
11.9691
0,5661
6.4727
0,5475
Table 4: Mean square error between reference denoising method and CLEEGN (MSE), AUC score of CLEEGN (AUC) in "BCI-Challenge" ERN dataset Part1
Method Subject
ASR-16
ASR-16-ICA
ASR-8
MSE
AUC
MSE
AUC
MSE
AUC
S02
4.2217
0.8205
3.5384
0.8425
4.0614
0.8205
S06
4.4036
0.5513
3.2639
0,6388
3.9688
0.6388
S07
11.4515
0.7116
5.1372
0.6588
8.2052
0.6717
S11
8.4986
0.5803
4,9902
0.6061
7.0836
0.5983
S12
7.1270
0.6980
4.1351
0.6690
4.6099
0.7005
S13
5,0222
0.7870
6.5591
0.7680
4.5217
0.7778
S14
2.2373
0.8137
2.5573
0.7723
2.6699
0.7982
S16
9.6206
0.6680
4.9104
0.6554
6.8716
0.6660
S17
18.8896
0.8076
10.9239
0.8041
27.6019
0.8141
S18
5.2366
0.7139
3.6065
0.7264
4.3582
0.7006
S20
6.7533
0.6843
5.5760
0.6668
4.3853
0.6776
S21
8.5629
0,6501
6.6647
0.6338
6,4900
0.6674
S22
9.3490
0,6395
5.1241
0.6460
6.7729
0.6419
$23
3.1867
0.6795
4,4331
0.6874
2,4365
0.7022
S24
2.7711
0.7191
3.7558
0.7130
3.7320
0.7117
S26
7.6073
0.5861
6.4456
0.5648
9.1152
0.5652
Table 5: Mean square between reference denoising method and CLEEGN (MSE), AUC score of CLEEGN (AUC) in "BCI-Challenge" ERN dataset Part2
14

---

Under review as a conference paper at ICLR 2023
Method
ASR-8-ICA
ASR-4
ASR-4-ICA
Subject
MSE
AUC
MSE
AUC
MSE
AUC
S02
3.4726
0.8277
4.0551
0.8204
3.3401
0.8249
S06
2.9740
0.6257
4.4043
0.5893
4.1949
0,6526
S07
5.4224
0.6423
8.4744
0.6592
4.7362
0.6969
$11
4.3832
0.6035
9.2528
0.5868
4.9227
0.5972
S12
3.5382
0.6800
3.9801
0.6969
3.8259
0.6732
S13
5.4227
0.7921
5.5527
0.7833
6.5429
0.7628
S14
2,1820
0.8004
3.6646
0.7947
2.9259
0,8057
S16
4.8361
0.6685
8.4332
0.6379
4.0452
0.6757
S17
11.1061
0.8014
24.9330
0.8102
16.4403
0,8141
S18
3,4891
0.7191
4.0793
0.7468
3.2979
0.7322
$20
4.6687
0.6625
4.9512
0.6444
4.6338
0.6505
S21
6.1690
0.6518
5.0372
0.6479
4.5595
0.6507
S22
5.1410
0.6399
6.7985
0,6566
5.5497
0,6801
S23
3.5369
0.6935
3.6311
0.6993
3,9354
0.6870
S24
2.8299
0.7215
4.2418
0.7231
3.7892
0.7075
S26
6,8030
0.5495
12.7965
0,5704
7.7062
0.5595
Table Mean square error between reference denoising method and CLEEGN (MSE), AUC score of CLEEGN (AÚC) in "BCI-Challenge ERN dataset Part3
Datalengthh (min)
ICLabel
30
20
10
Subject
AUC
MSE
AUC
MSE
AUC
MSE
AUC
S02
0.8114
3.4818
0.8290
3.6152
0.8359
3.5139
0.8191
S06
0.6281
2.4449
0.6117
2.4496
0.6195
2.3353
0.6215
S07
0.7298
4.1740
0.7177
4.3742
0.6744
4.2118
0.7166
S11
0.5971
7.0916
0.6050
6.8226
0.6107
7.6580
0.6037
S12
0.6916
2.6238
0.6700
2.7464
0.6724
2.3652
0.6949
$13
0.7859
3.7257
0.7855
3.9133
0.7774
3.5441
0.7926
S14
0.7888
1.7715
0.7766
1.7445
0.7959
1.6376
0.8086
S16
0.6503
5.5278
0.6585
5.5853
0.6811
5.2618
0.6878
S17
0.8198
4,6574
0,8162
4.4208
0.8069
4.4521
0.8066
S18
0.6997
3.3821
0.6965
3.4677
0.6899
3.7442
0.7045
S20
0.6829
3.4097
0.6873
3.8692
0.6669
3.4116
0.6960
S21
0.6198
4.0377
0.6760
3.7572
0.6918
3.7251
0.6733
S22
0.6372
3.2582
0.6920
3.4192
0.6383
3.0667
0.6600
$23
0.6985
2.3770
0.6994
2.6990
0.7050
2.3144
0.7213
S24
0.7339
2.1982
0.7108
2.1782
0.7317
2.0520
0.7396
S26
0.5867
3,9663
0.5701
3,9898
0.5524
4.0011
0.5566
Table Mean square error between reference denoising method and CLEEGN (MSE), AUC score of CLEEGN (AUC) in "BCI-Challenge" ERN dataset trained by different data length Partl
15

---

Under review as a conference paper at ICLR 2023
Datal ength (min)
ICLabel
4
2
Subject
AUC
MSE
AUC
MSE
AUC
MSE
AUC
S02
0.8114
3.2609
0.8101
3.1745
0.8324
3.3467
0.8213
S06
0.6281
2.2442
0.6093
3.0822
0.6135
3.3718
0.6302
S07
0.7298
4.2698
0.6655
4.6784
0.6774
3.8478
0.7180
S11
0.5971
6,5970
0.6135
7.6816
0.6406
4.5129
0.6193
S12
0.6916
2.7294
0.6948
2.7830
0.6733
3.7798
0.6717
S13
0.7859
3.4267
0.7793
5.7493
0,8007
6.3488
0.7760
S14
0.7888
1.6493
0.7934
2.2690
0.7937
2.5422
0.7980
$16
0.6503
5.9354
0.6681
7.9410
0.6594
5.2486
0.6584
S17
0.8198
4.5170
0.7939
4.7344
0.7965
5,8205
0.7986
S18
0.6997
3.8781
0.7546
4.6024
0.7202
4.7593
0.7239
S20
0.6829
4.1724
0.6721
4.3053
0.6837
5.9014
0.6707
S21
0.6198
4.5480
0.6905
4.8047
0.6494
5.2452
0.6497
S22
0.6372
3.3430
0.6629
4.0498
0.6772
5.7725
0.6739
$23
0.6985
2.9178
0.6939
3.1362
0.6936
4.3290
0.6796
S24
0.7339
2.3747
0.7173
2.8420
0.7468
4.5555
0.7275
S26
0.5867
4.2618
0.5666
4.9736
0,5705
6.8117
0.5682
Table Mean square error between reference denoising method and CLEEGN (MSE), AUC score of CLEEGN (AUC) in "BCI-Challenge" ERN dataset trained by different data length Part2
Method Subject
ICLabel
CLEEGN
IC-U-Net
ID-ResCNN
MSE
AUC
MSE
AUC
MSE
AUC
MSE
AUC
S02
0.8238
3.5139
0.8274
4.4294
0.7816
5.7621
0.7598
S06
0.6616
2.3353
0.6926
4.1464
0.6132
5,4486
0.5601
S07
0.8263
4.2118
0.7410
4.3550
0.7003
6.5909
0.6848
S11
0.6083
7.6580
0.6324
5,0279
0.6153
5.1880
0,5729
S12
0.6947
2.3652
0.6756
3.8519
0.6587
7,0038
0.6283
S13
0.7913
3.5441
0.7835
8.1448
0.7469
8.1590
0.7509
S14
0.8128
1.6376
0.8135
2.9057
0.7674
3.7053
0.7679
S16
0.6402
5.2618
0.6615
4.0351
0.6405
6.1420
0.6485
S17
0.8177
4.4521
0.8153
8.2195
0.7612
7.8305
0.7564
$18
0.7419
3.7442
0.7667
4.7798
0.7082
6,4393
0.6729
S20
0.6962
3.4116
0.6770
5.7497
0.6802
8.9663
0.6359
S21
0.7130
3.7251
0.7742
5.1049
0.7132
7.4864
0.6718
$22
0.7166
3.0667
0.7435
6.1630
0.7199
10.6583
0.6749
S23
0.7016
2.3144
0.7316
4.3579
0.6848
3,6919
0.6524
S24
0.7366
2.0520
0.7228
3.7706
0.7186
4.7246
0.7245
S26
0.5663
4.0011
0.5445
8.2955
0.6077
9.6387
0.5539
Table 9: ERN mean square error and AUC score of each subject different network structure
16

---

Under review as a conference paper at ICLR 2023
Method Subject
ICLabel
CLEEGN
IC-U-Net
ID-ResCNN
MSE
Top-1 Acc
MSE
Top-1 Acc
MSE
Top-1 Acc
MSE
Top-1 1 Acc
S001
0.6930
0.0859
0.6620
0.0852
0.4570
0.1228
0,2335
S002
0.7350
0.1379
0.8000
0.0609
0.3235
0.0608
0.2315
S003
0.3175
0.1113
0.3645
0.1013
0.2830
0.1798
0.2325
S004
0.2610
0.1040
0.2770
0.0984
0.2380
0.1896
0.2330
S005
0,2870
0.0387
0.2985
0.0475
0.2685
0.0539
0.2310
S006
0.5300
0.0237
0.5595
0.0322
0.3910
0.0583
0.2290
S007
0.5110
0.0819
0.4935
0.0768
0.4035
0.1503
0.2340
S008
0,2020
0.1143
0.2270
0.1169
0.2015
0.1205
0.2345
S009
0.9050
0.0492
0.8005
0.0460
0.4070
0.3755
0.4070
S010
0,5560
0.0405
0.5055
0.0379
0.3690
0.3125
0.5260
S011
0,4360
0.0668
0.6870
0.0651
0.4225
0.6281
0.4075
Table 10: ture
SSVEP mean square error and top-1 accuracy of each subject different network struc-
17


Reviews:
[{"review_id": "pyT8vQSEAT", "reviewer": "Reviewer_KYwV", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "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": "", "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": "", "main_review": "", "summary_of_the_paper": "The paper proposes a method for EEG reconstruction based on CNN. ", "summary_of_the_review": "The paper proposes a method that is fairly simple simple based a very simple CNN. The lack of good baselines and weak interpretability claim make this paper hard to recommend.", "strength_and_weaknesses": "Strengths:\n- Performs well according to reported results\n\nWeaknesses:\n- The method is fairly simple simple based a CNN with mirrorred layers that are referred to as encoder and decoder\n- There are just two baselines and its surprising that such a simple model can outperform both references. Perhaps enough algorithmic work has not been done in the domain.\n- The reference models are also based on very old architecture, i.e. U-Net and ResNet.\n- The interpretability aspect makes little sense where PCA is used to project raw signals into a 2-D space and the relative positions are said to be similar.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "f5GRoWswNc", "reviewer": "Reviewer_fmQB", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "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": "", "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": "", "main_review": "", "summary_of_the_paper": "The authors present an approach to utilize a convolutional neural network (CLEEGN) with limited novelty (there are so many such approaches on the market already) and super limited evaluation on a tiny EEG dataset. Due to little originality and minimal assessment, the paper is not suitable for the ICLR.", "summary_of_the_review": "The authors, unfortunately, submitted a very preliminary work lacking technical, novelty, and implementation details. A limited evaluation and small classification gain further support for a vague idea evaluation. At ICLR, more novel and well-supported results interest the audience. ", "strength_and_weaknesses": "The application of deep learning to EEG denoising looks interesting, but novelty in the proposed approach is lacking. The authors seem aware of ICA and EEGNet, but other successful strategies, such as EMD or BrainDecode are unfortunately ignored. Another submission limitation is a minimal and not-that-perfect evaluation on a tiny open-access dataset. The proposed CLEEGN is a standard and of the shelf available convolutional encoded + decoder, thus hard to find novelty in the proposed approach. ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "YrhwOfoEZT4", "reviewer": "Reviewer_8xpw", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "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": "", "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": "", "main_review": "", "summary_of_the_paper": "In this paper, the authors proposed a novel convolutional neural network (CLEEGN) for plug-and-play automatic EEG reconstruction. CLEEGN can reconstruct subject-independent EEG without any training/calibration for a new subject. The performance of CLEEGN was validated using multiple evaluations including reconstruction error assessment, decoding accuracy, etc. In general, the paper is well-written and easy to understand.", "summary_of_the_review": "The innovation of this work is general. The proposed model aims to achieve online EEG artifact removal and reconstruction. A lightweight autoencoder-based CNN was used and performance was verified by a variety of evaluations including waveform observation, reconstruction error evaluation, and decoding accuracy. The paper is likely to have a modest impact within a subfield of AI.", "strength_and_weaknesses": "Strength:\n1.A lightweight autoencoder CNN is proposed for EEG reconstruction.\n2.The model can reconstruct subject-independent EEG without any training/calibration for a new subject.\n3.The model outperforms leading methods in providing reconstructed EEG data with the optimal decoding performance for some BCI datasets.\n\nWeaknesses:\n1. In Figure 1, the differentiation of each part in the encoder and decoder is not clear and it is recommended that it be redrawn.\n2. How can the Decoder part of the model process data of size [1*T*C] and output a result of size [C*T*1] using a convolution kernel of size (C,1)?\n3. Is the purpose of denoising and reconstructing to approximate the effect of the offline denoising method? The reference EEG is demanding and how to ensure that the reference EEG is clean.\n4. There are certain errors in the Decoder module in Table 1.\n5. The experimental setup is unclear. How are the data sets divided? Is the data used after some pre-processing, or is it raw data without any processing? How are the model parameters set? \n6. The experimental results of EEGNet on the raw data should be added to Tables 2 and 3.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "9yqjSbyYX3o", "reviewer": "Reviewer_sQXi", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "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": "", "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": "", "main_review": "", "summary_of_the_paper": "The work proposes an online deep-learning encoding-decoding model for EEG denoising/artifact removal. The work uses established offline artifact removal methods to create training data, so the data after offline artifact removal is considered the reference ground truth data that should be reconstructed from the noisy EEG signal. The reconstruction results are evaluated by mean squared error to the reference data and by the decoding accuracies achieved on the reconstructed/cleaned data.", "summary_of_the_review": "This seems a clear straightforward method with a clear evaluation, further evaluation on more data may increase the scientific value of the manuscript further.\n", "strength_and_weaknesses": "**Update** \n\nThanks for adding relevant discussion points to the paper, good that it is at least addressed a bit.\n\n\n**Pre-Rebuttal**\n\nThe idea is straightforward, clearly explained and clearly evaluated.\n\nOne open question to me is how far the training setup of using the results of offline artifact removal as the ground truth limits the potential performance of the overall method. Could the authors also envision instead synthetically adding artifacts to clean EEG (potentially also synthetic) data? Not necessarily as part of this manuscript, but these questions would be worth discussing a bit.\n\nAlso more datasets and cross-dataset performance may be interesting, like would it be possible to go towards a general-purpose reconstruction method that can be applied to a new dataset without training? Some discussion on that could be interesting as well.\n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject