EEG-MACS: Manifold Attention and Confidence Stratification for EEG-based Cross-Center Brain Disease Diagnosis under Unreliable Annotations

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-09 06:07:53

Abstract: Cross-center data heterogeneity and annotation unreliability significantly challenge the intelligent diagnosis of diseases using brain signals. A notable example is the EEG-based diagnosis of neurodegenerative diseases, which features subtler abnormal neural dynamics typically observed in small-group settings. To advance this area, in this work, we introduce a transferable framework employing **M**anifold **A**ttention and **C**onfidence **S**tratification (**MACS**) to diagnose neurodegenerative disorders based on EEG signals sourced from four centers with unreliable annotations. The MACS framework’s effectiveness stems from these features: 1) The _**Augmentor**_ generates various EEG-represented brain variants to enrich the data space; 2) The _**Switcher**_ enhances the feature space for trusted samples and reduces overfitting on incorrectly labeled samples; 3) The _**Encoder**_ uses the Riemannian manifold and Euclidean metrics to capture spatiotemporal variations and dynamic synchronization in EEG; 4) The _**Projector**_, equipped with dual heads, monitors consistency across multiple brain variants and ensures diagnostic accuracy; 5) The _**Stratifier**_ adaptively stratifies learned samples by confidence levels throughout the training process; 6) Forward and backpropagation in **MACS** are constrained by confidence stratification to stabilize the learning system amid unreliable annotations. Our subject-independent experiments, conducted on both neurocognitive and movement disorders using cross-center corpora, have demonstrated superior performance compared to existing related algorithms. This work not only improves EEG-based diagnostics for cross-center and small-setting brain diseases but also offers insights into extending MACS techniques to other data analyses, tackling data heterogeneity and annotation unreliability in multimedia and multimodal content understanding. We have released our code here: https://anonymous.4open.science/r/EEG-Disease-MACS-0B4A.

Main paper: 
 introduction: 
 Addressing the challenges posed by data heterogeneity and annotation unreliability is essential for the analysis and interpretation of multimedia and multimodal data [1, 2] . These issues are particularly prevalent in human-centered computing, where they encounter even greater difficulties in small-group settings. A prime example is the analysis of neural dynamics, such as the automated diagnosis of cognitive and movement brain disorders through electroencephalogram (EEG) signals [3, 4] . While EEG-based diagnosis models have undergone extensive research, they are often disease-specific, rely on single-center studies, and depend on fully supervised learning with annotated data [5] . Developing effective and transferable models capable of handling cross-center data and low-quality annotations is crucial for enhancing adaptability across different diseases and advancing EEG modeling techniques.Current EEG analyzing methods range from traditional feature engineering to deep learning approaches. Various features extracted from domains like time (e.g., Hjorth parameters), frequency (e.g., Fourier Transform), dynamics (e.g., entropy), and functional networks (e.g., synchronization and phase coupling) have demonstrated efficacy due to EEG's intrinsic characteristics [6] [7] [8] [9] . The sparsity of handcrafted features has led to the adoption of deep learning models, such as convolutional neural networks, graph neural networks, and hybrid approaches, to capture spatial-temporal patterns, structured characteristics, and other high-level representations [10] [11] [12] [13] . However, these existing methods often struggle with noisy data and lack cross-center applicability. This may primarily be due to: 1) the ineffective mapping that cannot project EEG signals into a high-level feature space invariant across diverse data distributions from multiple centers; 2) the absence of integrating advanced learning strategies to tackle complex learning issues under unreliable annotations effectively.From this perspective, our work presents a novel framework aimed at developing a transferable model adept at managing unreliable annotations, as illustrated in Figure 1 . This model specifically targets the recognition of neurodegenerative disorders. These include neurocognitive disorders, represented by mild cognitive impairment(MCI) and Alzheimer's Disease(AD), and movement disorders, exemplified by Parkinson's Disease(PD). We selected these three diseases due to the potential progression relationship between AD and MCI [14] as well as the observed comorbidity between PD and cognitive impairment [15, 16] . The proposed framework is characterized by its innovative use of Manifold Attention and Confidence Stratification (MACS), which optimally synergizes modules including the Augmentor, Switcher, Encoder, Projector, and Stratifier. MACS's success in achieving transferability across centers and diseases under unreliable annotations can be attributed to three key factors: 1) The establishment of an optimized mapping that leverages the strengths of both Euclidean and Riemannian manifold spaces enables the model to extract more effective EEG representations.2) The integration of supervised and self-supervised learning strategies through confidence stratification to address the issue of learning from unreliable annotations. 3) An effective encoder enhances the representation space, which improves confidence stratification accuracy, thereby enabling the encoder to develop more robust and inductive representations in a mutually reinforcing cycle. Our primary contributions are outlined below:• Introducing a novel EEG-based framework, MACS, designed for learning from unreliable annotated EEG signals with cross-center transferability.Augmentor: Enriches the data space via augmentation. Switcher: Mitigates overfitting on incorrect labels. Encoder: Integrates Manifold and Euclidean spaces to improve EEG representation learning. Projector: Features dual heads to assess both data consistency and diagnostic accuracy. Stratifier: Stratifies data based on confidence levels.• Establishing confidence stratification-based constraints for MACS's forward and back-propagation that create a selforganizing system to enhance representation learning and confidence assessment, thereby fostering a virtuous cycle.• Demonstrating MACS's superior performance in learning from unreliable annotations compared to state-of-the-art (SOTA) methods, validated across two types of diseases and through cross-center testing and fine-tuning. • Making the code public to contribute multimedia community. 
related work 2.1 learning with unreliable annotations: 
 Noisy label learning contains model-free and model-based strategies aimed at reducing the influence of incorrect labels. These strategies involve estimating noise patterns and conducting supervised learning with clean samples [17] , as well as handling noisy labels and refining the model by addressing internal conflicts [18, 19] . Such methods depend on the analysis of the relationships within noisy data. Recent research in time-series [20] and image domains [21, 22] applies unique principles to delineate relationships. In the image domain, Promix [21] adopts small-loss criteria and prediction consistency to filter high-confidence examples, followed by learning through Debiased Semi-Supervised Training. Sel-CL [22] employs nearest neighbors to select confident pairs for supervised contrastive learning. In the time-series domain, CTW [20] selects confident examples based on small-loss criteria and applies time-warping to these instances to learn more robust representations. We use these to benchmark MACS and, following [23, 24] , incorporate mix-up techniques [25, 26] to enhance learning from noisy labels.Contrastive learning has demonstrated effectiveness in both unsupervised (e.g., SimCLR [27] ) and supervised (e.g., SupCon [28] ) contexts. It provides valuable self-learning strategies for pre-training in time series data, such as enhancing time-frequency [29] and cross-sample temporal consistency [3] . However, the impact of consistency pre-training on handling unreliable annotated data is less pronounced. Augmentation-based contrast [30] shows promise for semi-supervised learning with missing labels but is less effective without true label priors. To overcome these limitations, MACS introduces multi-view contrastive learning, guided by estimated confidence levels.Manifold-based modeling excels in EEG signal analysis for braincomputer interfaces (BCI) [31, 32] , leveraging Riemannian geometry for high-dimensional neural data representation through affineinvariant geometric distances [33, 34] . Using EEG's Riemannian structure for BCI domain-adaptation [10, 35, 36] and multi-task application [11] indicates the potential of Manifold geometry learning in identifying robust latent spaces. Such evidence bolsters the Encoder's design in MACS. Our work advances current geometry learning by integrating dynamic functional networks with crossattention mechanisms. Additionally, findings from our pilot study suggest that when integrated with contrastive learning, Riemannian geometry learning offers a promising alternative to GNNs [37] .  
macs: 
 In this section, we elucidate the methodology for learning from brain signals annotated with unreliable labels using the Manifold Attention and Confidence Stratification (MACS) framework. As depicted in Figure 2 , MACS handles unreliable labels and enhances cross-center transferability through its modules (in Section 3.1), adaptive constraints (in Section 3.2), and training objective(in Section 3.3).Assuming that brain activity is monitored using 𝑑 sensors at a sampling frequency of 𝑓 𝑠 for a duration of 𝑡 seconds, we obtain the following set of observations as the input for the MACS framework:EQUATIONwhere 𝑇 = 𝑓 𝑠 • 𝑡 denotes the total number of sampling points, and 𝑛 ∈ {1, 2, . . . , 𝑁 } represents a specific individual from a total of 𝑁 subjects under observation. The MACS framework identifies brain states as Ŷ (𝑛) = 𝐹 Θ (𝑨 (𝑛) 𝑑,𝑇 ) in scenarios with unreliable annotations 𝑌 (𝑛) , utilizing five key components: 1) Augmentor, 2) Switcher, 3) Encoder, 4) Projector, and 5) Stratifier. The initial four modules are integral to the framework's forward propagation, while the Stratifier systematically regulates the learning mechanism.Augmentor: Producing Brain Variants through Dual Random Transform. In this module, data augmentation is applied to the preprocessed brain data as described in [29] , following the equation:EQUATIONwhere 𝝐 𝑖 𝑗 ∼ N (0, 𝜎 2 ) is independently and identically distributed, with 𝜎 representing the standard deviation. Additionally, 𝐴 𝑑,𝑇 𝑠 is a segmented, non-overlapping fragment of 𝑨 𝑑,𝑇 , utilized to reduce computational load and increase the system's robustness. Two random transformations are concurrently executed, resulting in dual brain variants. Differing from the method of specific weak and strong augmentations for two views [38] , our approach operates a random configuration, leading to improved performance.Switcher: Blending-based Variant Generation Conditional on Confidence Levels. In the Switcher module, dual brain variants undergo selective processing. As in Figure 3 , distrusted inputs are directly forwarded to subsequent modules, while trusted ones are routed to a blender for generating interpolated samples. Inspired by the benefits of sample and network mixing, as discussed in [25] and [23] , and aiming to enhance representation learning with partial labels and reduce confirmation bias in noisy data, we distort each trusted sample 𝐴 * 𝑑,𝑇 𝑠 and its corresponding label 𝑦 * by interpolating it with another randomly selected sample (𝐴 + 𝑑,𝑇 𝑠 , 𝑦 + ), to produce blended variants, as described in Eq.( 3). 𝜆 is sampled from a uniform Beta(1, 1) distribution and adjusted to ensure it is always at least 0.5 by setting it to the greater value between 𝜆 and 1 -𝜆.EQUATION)This choice is implemented to balance the retention of the primary input's information and the introduction of diversity from the randomly indexed input throughout the mixing process.Differing from the referenced studies [23, 25] , our method presents two distinctions: 1) We produce dual-blended brain variants while employing shared network modules, thus effectively decreasing model complexity. 2) We initiate data blending exclusively on samples exhibiting higher confidence levels, aiming to mitigate concerns related to unreliable labels. The confidence level associated with each sample in the mini-batch is evaluated using the Stratifier module in the MACS framework. With evidence from learning Riemannian geometry for structured brain data as shown in [11, 36] , a manifold converter 𝑔 𝑚𝑓 𝑑 transforms dynamic network Φ into a set of symmetric positive definite (SPD) matrices, 𝑆 ++ 𝑑 (R), thus defining a Riemannian manifold M. This transformation involves eigenvalue decomposition and transpose manipulation of each adjacency matrix, represented as D = 𝑔 𝑚𝑓 𝑑 (Φ). To measure those 𝑆 ++ 𝑑 , we utilized the Log-Euclidean Riemannian metric [33] , as it serves as the first-order approximation of the Affine-Invariant metric. At each point D 𝑡 𝑖 on the manifold M, a tangent space is defined by log : M → T M , where the inverse is operated by exponential mapping, as illustrated in Figure 4 . The distance between any two points on the manifold M is then calculated using the following equation:EQUATION)Utilizing this metric to reflect the correlation between points D 𝑡 𝑖 ∈ 𝑆 ++ 𝑑 , we constructed a manifold-based dynamic attention block 𝑔 𝑑𝑎𝑡𝑡 , following the approach in [11] , but with modifications to incorporate a cross-temporal attention mechanism. This integration of dynamic relationships is mathematically represented as: (5) where 𝛿 scales the values to a range of 0 to 1. 𝑓 𝑊 𝑘 represents the operation of 𝑊 𝑘 multiplied by a matrix and then by 𝑊 ⊤ 𝑘 . This can be expressed as 𝑓 𝑊 𝑘 (𝑋 ) = 𝑊 𝑘 𝑋𝑊 ⊤ 𝑘 . Similarly, 𝑓 𝑊 𝑞 and 𝑓 𝑊 𝑣 operate in the same manner, where 𝑊 𝑞 , 𝑊 𝑘 , and 𝑊 𝑣 ∈ R 𝑑 ×𝑑 1 are learned weight matrices used for the bilinear mapping. Subsequently, the fused feature matrices F = {F 𝑖 } are subjected to a nonlinear activation process, involving the rectification of eigenvalues, as detailed in [33] . To enable the measurement of these features F in Euclidean space for further processing, the embeddings F are obtained by mapping F onto the tangent space, followed by flattening and concatenating them together. Stratifier: Measuring Latent Similarity for Confidence Stratification. This module 𝑓 𝑆𝑡 plays a crucial role in assessing the reliability of the learned representations, which in turn influences the learning mechanism of the MACS framework. It categorizes the confidence levels of samples based on Z through the following steps:F 𝑖 = 𝐼 ∑︁ 𝑗=1,𝑗≠𝑖 𝛿 𝑑 (𝑓 𝑊 𝑘 (D 𝑡 𝑖 ), 𝑓 𝑊 𝑞 (D 𝑡 𝑗 )) • log(𝑓 𝑊 𝑣 (D 𝑡 𝑖 )),• Measuring the cosine similarity between samples in Z.EQUATION• Locating the K closet neighbors of each sample 𝑧 𝑖 in Z using the k-Nearest Neighbors method. • Obtaining a neighbor-determined label ȳ for each sample 𝑧 𝑖 by averaging the original labels of its neighbors.EQUATIONwhere N 𝑖 denotes the neighbourhood of K samples to 𝑧 𝑖 . The indicator function, denoted as 1 𝐵 , is a function that returns 1 when the condition 𝐵 is satisfied and 0 otherwise.• Considering a sample trusted if its neighbor-determined label matches its original label. Following [40] , we also adopted a dynamic thresholding approach to ensure class balance. • Identifying trusted pairs Ψ from trusted samples Z crd based on label identity.For the distrusted samples Z dst = Z \ Z crd , corresponding auxiliary labels are generated through the forward-propagation process 𝑨𝑓 𝐸𝑛 ---→ F 𝑓 𝑃𝑐 ---→ Ŷ .MACS constraints are dependent on the confidence stratification adaptively assessed by the Stratifier module during training, which is pivotal in stabilizing representation learning in the context of unreliably annotated data. Intuitively, they regulate the behavior of the modules, influencing the forward propagation of features and the computation of backward losses, as depicted in Figure 2 . Four types of constraints are implemented within the framework: they are applied to both the Augmentor and Switcher modules, and also influence the multi-view contrastive loss and discriminative loss. Those constraints work as follows:1) The first constraint specifically controls the gradients of Switcher module ∇𝑓 𝑆𝑤 . It effectively utilizes linearly interpolated samples to enrich data associated with trusted labels while simultaneously mitigating the risk of being misled by unreliable data.2) The second constraint type facilitates a 'divide and conquer' approach within the multi-view contrastive loss. Specifically, contrastive learning is initiated within three groupings: among trusted pairs Ψ, among blended variants of trusted samples Z crd , and among dual random brain variants Z.3 Projector: Z = 𝑓 𝑃𝑙 ( F ), E = 𝑓 𝑃𝑐 ( F ) 8:Loss: Compute self-supervised contrastive loss L 𝐴𝑔 and use softmax prediction Ŷ without data augmentation to compute discriminative loss L 𝐷𝐿 Encoder: F = 𝑓 𝐸𝑛 (𝐴 𝑑,𝑇 𝑠 )Projector: Z = 𝑓 𝑃𝑙 ( F ), E = 𝑓 𝑃𝑐 ( F )Loss: Compute mixed self-supervised loss L 𝑆𝑤 , supervised contrastive loss L 𝑆𝑡 , and use original label 𝑌 without data augmentation to compute discriminative loss L 𝐷𝐿 end if 15: end for to generate auxiliary labels, providing alternatives to unreliable labels for backpropagation.4) The fourth constraint conditionally influences the discriminative loss. It evaluates the errors of trusted samples using their original labels, while the gradients for distrusted samples are computed based on auxiliary labels.The overall training objective, as formulated in Eq.( 8), is an equal sum of contrastive and discriminative losses.EQUATIONLoss. In our framework, the contrastive loss L 𝐶𝐿 consists of three components: L 𝐴𝑔 , L 𝑆𝑤 , and L 𝑆𝑡 , each addressing different aspects of the data. All components adhere to the core formulas defined in Eq.( 9). The computation involves aggregating 𝐶 𝑖,𝑗 for all pairs of samples 𝑥 𝑗 within the same minibatch that share the same label as 𝑥 𝑖 (i.e., 𝑦 𝑖 = 𝑦 𝑗 ), across all 𝑁 𝑦 𝑖 such samples. Each minibatch contains 𝑁 𝑚 samples, excluding selfcontrast cases where 𝑖 = 𝑗. A temperature parameter 𝜏 modulates the scale of the dot products in the softmax denominator.EQUATION1) L 𝐴𝑔 : It aims to minimize the distance between dual augmentations for distrusted examples Z dst , which constitutes a selfsupervised learning approach.2) L 𝑆𝑤 : This targets the enhancement of similarity among blending variants, but exclusively for trusted samples Z crd , which constitutes a mixed self-supervised learning approach.3) L 𝑆𝑡 : This component incorporates interpolation supervised contrastive learning techniques, as exploited in [22, 40] , and shares the same hyperparameter 𝜆 with Eq. (3) (10) . In this equation, 𝑌 (𝑛) represents the original label, Ŷ (𝑛) denotes the generated auxiliary label. Algorithm 1 presents the pseudocode for MACS.L 𝐷𝐿 𝑖 = -𝜆 Ỹ ( * )𝑇 log(E (𝑖 ) ) -(1 -𝜆) Ỹ (+)𝑇 log(𝑓 𝑃𝑐 (E (𝑖 ) ), Ỹ (𝑛) = 𝑌 (𝑛) , if 𝐴 (𝑛) 𝑑,𝑇 𝑠 ∈ 𝐴 𝑐𝑟𝑑 , Ŷ (𝑛) , if 𝐴 (𝑛) 𝑑,𝑇 𝑠 ∈ 𝐴 𝑑𝑠𝑡 . (10) 
experiments: 
 We conduct four subject-independent experiments (presented in Figure 2 ) to validate MACS's performance in learning from unreliable annotations and transferability: 1) Three-fold cross-validation using public PD data from Center A (Model Θ 𝑃𝐷 ); 2) Four-fold cross-validation with MCI (MCI due to AD) data collected from Center C (Model Θ 𝑀𝐶𝐼 ); 3) Direct testing Θ 𝑃𝐷 on a cross-center PD corpus (Center B) for transferability evaluation; 4) Fine-tuning Θ 𝑀𝐶𝐼 on a cross-center AD corpus (Center D) for transferability assessment. Please refer to Appendix for implementation details.PD Datasets. The PD data includes two sets. The first set, from the University of New Mexico(NMU), comprises data from 27 patients and 27 controls [7] . The second set, from the University of Iowa(IU), includes data from 14 patients and 14 controls [42] . Both datasets were acquired using 64-channel Ag/AgCl electrodes with the Brain Vision system at a sampling rate of 500 Hz. MCI Dataset. The MCI dataset, from a local hospital in City C, includes data from 46 MCI patients and 43 age-matched normal controls (NC), recorded in an eye-closed state. Data was captured using a 64-channel Ag/AgCl electrodes Brain Product system at a sampling rate of 5000 Hz.AD Dataset. The AD dataset, from a local hospital in City D, comprises data from 20 AD patients and 20 age-matched NC volunteers. Each participant provided two EEG samples (eye-closed and eye-open states), recorded using a 16-channel Ag/AgCl electrodes Symtop system at 1024 Hz.For each dataset, we created a set of unreliable labels, assigning incorrect labels to a percentage of the samples defined by 𝛼. Performance was evaluated using true labels, verified, and corrected by experts. Results for 𝛼 = 0.3 are presented as main findings, while those for 𝛼 = 0.5 are detailed in the Appendix. The MACS model was trained over 30 epochs using Stochastic Gradient Descent with a momentum of 0.9 and a weight decay of 10 -4 . A step-learning rate scheduler improved training efficiency by starting at 0.1 and reducing it by a factor of 0.1 every 10 epochs. Similarly, a three-fold cross-validation was executed on the PD dataset from the NMU center, with a batch size of 60 We conducted four-fold cross-validation on the MCI dataset with a batch size of 128, and three-fold crossvalidation on the PD dataset from the NMU center with a batch size of 60. The MACS framework was developed on a Linux-based system using PyTorch (version 2.0.0), with hardware including a GeForce RTX 3090 GPU and an Intel i9-12900K CPU.Feature engineering insights have led to the development of the MACS Encoder. EEG frequency markers [7] are inductive for PD but found less effective for MCI. EEG complexity measured by the entropy [8] and Hjorth parameter [6] enhanced MCI detection but was less effective than frequency domain features in PD cases. We compared the functional connectivity characterized by Pearson correlation (Corr) in Euclidean space [41] and SPD in manifold space [36] and found the advantage of manifold representations. This evidence has informed the integration of a specialized block in the Encoder to process underlying temporal-spectral-spatial features and analyze synchronization characteristics through manifold geometry.We benchmarked MACS against SOTA methods, evaluating its performance in scenarios with unreliable annotations and independently assessing the Encoder in scenarios with clean annotations (see Table 2 ). For the clean data learning comparison, we employed advanced EEG encoders that target manifold geometry [10] , integrate attention mechanisms [11] , and utilize contrastive learning [3] . Given the absence of a dedicated SOTA model for EEG data with unreliable annotations, we compared MACS against leading methods in the time series [20] and natural image domain [21, 22] . Specifically, we replaced their encoders with our Encoder in MACS to validate the efficacy of our combined manifold-Euclidean representation learning strategy for EEG signals. Overall, MACS's weakly supervised learning mechanism, combined with an effective Encoder, enables superior comprehensive performance across two disease types. We unveil MACS's learning process through t-SNE mapping in Figure 5 (see Appendix for the PD case). Observations indicate that MACS gradually selects the correct trust samples, enhancing representation learning and forming class-specific clusters.Memory Length. The multi-view contrastive loss is crucial for the MACS framework. Effective contrastive learning heavily depends on the adequacy of positive and negative pairs; therefore, inspired by [40] , we proposed maintaining a memory of previous steps to circumvent the constraints imposed by batch size, enabling more extensive use of available small-scale data. For example, we summarize the research findings for MCI in Table 3 , suggesting that memory length contributes to learning efficiency, but its relationship is not monotonic. Please note that the methods involving contrastive learning are discussed in Table 2 , where we also determined their optimal performance to ensure a fair comparison.Temporal Scale Temporal scale refers to the time interval for constructing dynamic functional networks in the Encoder. The findings indicate that a second-level temporal scale is crucial for capturing patterns. For example, results from the MCI dataset are included in Table 3 . More effective EEG markers that appear at larger temporal scales may be attributed to the 'slowing' phenomenon in brain From cross-validation tests on PD(NMU) and MCI datasets, we derived N-fold MACS models for PD (Θ 𝑃𝐷 ) and MCI (Θ 𝑀𝐶𝐼 ). We assessed MACS's transferability by testing {𝜃 𝑖 𝑃𝐷 } 2 𝑖=0 on a crosscenter PD corpus (from IU), and fine-tuning {𝜃 𝑖 𝑀𝐶𝐼 } 3 𝑖=0 on a crosscenter AD corpus. To address the spatial resolution gap between MCI and AD data, we employed EEG's interpolation technique [45] . The averaged evaluation results from Θ 𝑃𝐷 , halted at both optimal and final epochs (Figure 6  
 
 Appendix:  


Decision: This paper explored the automated diagnosis of brain disorders from the perspective of heterogeneity and unreliability issues from EEG data. All reviews are positive after the rebuttal stage, and the authors need to revise the paper and close these concerns in the final version.

Reviews: review: paper_summary: paper_summary: This paper focuses on the challenges of cross-center data heterogeneity and unreliable annotations in the intelligent diagnosis of diseases using brain signals. It proposes a transferable framework employing manifold attention and confidence stratification to diagnose neurodegenerative disorders based on EEG signals sourced from four centers with unreliable annotations. Experiments verify the effectiveness of the proposed model on EEG-based diagnostics.
strength_weakness: strength_weakness: 1. The paper studies the challenging issues of cross-center data heterogeneity and unreliable annotations, which is interesting and contributive to the community.
2. The presentation of the proposed model is clean. It introduces the confidence stratification-based constraintsto enhance representation learning andconfidence assessment, which is interesting.
3. Extensive experiments show the superiority of the proposed model, verifying the effectiveness of the proposed model on EEG-based diagnostics.
limitations: limitations: 1. The proposed model is not straightforward or self-motivated. The correspondence between the proposed model and the targeted challenges is not well established.
2. The introduction of the proposed model is complicated and unclear. The time complexity of the proposed model is not explored.
3. The introduction to previous work related to tackling cross-center data heterogeneity is not 
previous work related to the 
4. Although extensive experiments are performed, it lacks convincing experiments or visualizations to demonstrate the insights behand the model.
paper_summary: paper_summary: This work introduces MACS, a framework that leverages Manifold Attention and Confidence Stratification to address data heterogeneity and annotation unreliability in EEG modeling. Specific advantages and disadvantages are shown below.
strength_weakness: strength_weakness: 1. The figure of the article is pleasing to the eye.
2. The structure and logic of the article is clear.
3. The description of the content study is very full.
limitations: limitations: I will raise the score if the questions are answered carefully and satisfactorily.
1. Your article doesn't really have anything to do with multimodality or multimedia, but you make a point of emphasizing the article's relationship to multimedia and multimodality at the end of the abstract and at the beginning of the introduction but never mention it again in the rest of the article, so your mention seems very deliberate, explain it.
2. You mention “the absence of integrating advanced learning strategies to tackle complex learning issues under unreliable annotations effectively.” at the end of the second paragraph of the introduction, which is not a valid reason. Combined with the fact that your approach incorporates multiple modules, this approach makes it look like you're piling up the latest strategies into a single approach, so I can think of your approach as insufficiently innovative.
paper_summary: paper_summary: The paper addresses the challenges posed by data heterogeneity and unreliable annotations in the analysis and interpretation of multimedia and multimodal data, particularly in human-centered computing and small-group settings. It focuses on the automated diagnosis of cognitive and movement brain disorders using electroencephalogram (EEG) signals, which is complicated by disease-specific models, single-center studies, and the need for fully supervised learning with annotated data. Authors propose a novel framework called Manifold Attention and Confidence Stratification (MACS), aimed at developing a transferable model that can manage unreliable annotations. The paper demonstrates MACS's superior performance compared to state-of-the-art methods in learning from unreliable annotations, validated across different diseases and through cross-center testing and fine-tuning.
strength_weakness: strength_weakness: The authors make their code publicly available which is of significant broader impact to the community. The authors test their proposed approach on multiple datasets comprehensively and through ablation studies. Moreover, the authors present comparison to existing works for demonstration of superiority of their proposed approach.
limitations: limitations: The readability of the paper could have been significantly improved, which hinders the clarity of parts of the paper significantly. Some parts of algorithm 1 are not clear such as the utility of the augmentor generated samples. The idea that augmentor augments both trusted and untrusted samples needs justifications. There are a number of selections and statements which should be better justified.
paper_summary: paper_summary: This paper proposes a method for the identification of neurodegenerative diseases, employing Manifold Attention and Confidence Stratification (MACS) to build a transferable network that addresses to some extent the heterogeneity of cross-center data and the unreliability of annotations.
strength_weakness: strength_weakness: 1. The overall framework proposed by the article is very innovative, and it is introduced in great detail.
2. The connections and logic among the various modules proposed in the article are very tight, progressively constructing the overall framework. 
3. The graphical annotations in the article are rich and clear, facilitating easy reading and understanding. 
4. The author conducted thorough comparative experiments and meticulously categorized the main methods used in each comparison experiment.
limitations: limitations: 1. The author mentions comparisons with other methods in the Switcher section, but these are not reflected in the experimental section. 
2. The author extensively uses dual-branch structures in the framework construction; could this usage adversely affect the model's speed and parallelism?
3. Some expressions in the article are technical and significant, but they lack fluency and readability, such as the ‘the ineffective mapping that cannot project EEG signals into a high-level feature space invariant across diverse data distributions from multiple centers;’ in the Introduction section.
paper_summary: paper_summary: This paper introduces the main features and advantages of the MACS framework, including data augmentation, feature space enhancement, an encoder based on Riemannian manifold and Euclidean metrics, a dual-head projector, and sample stratification based on confidence levels. Through these characteristics, the framework has improved performance across different centers and validations, especially in the areas of neurocognitive and movement disorders.
strength_weakness: strength_weakness: The MACS framework proposed in the article has good cross-center data applicability, specifically designed to handle EEG data from different centers, which may vary in collection conditions, equipment, and patient populations.

The framework is capable of processing EEG signals with unreliable annotations, due to its adoption of sample stratification based on confidence levels and self-supervised learning strategies to mitigate the impact of incorrect labels.
limitations: limitations: The specific form of input data is unclear: the EEG sampling equipment and protocols vary across centers, so how many channels are set for the model's input data? What is the sampling rate? What is the data length? For a cross-center model, is it necessary to standardize the number of channels, sampling rate, and data length?

EEG records the noisy electrical activity of a group of neurons, and the authors claim that a temporal scale design of 2 seconds can better capture patterns. Intuitively speaking, a 2-second temporal scale only retains low-frequency information and loses the rich dynamic frequency information of the temporal neural signals.
The article proposes a method for dealing with unreliable annotations, but does not elaborate on how to quantify or assess the unreliability of the annotations.

What does "w/o MA" in Table 4 represent?

It is recommended to modify the author's GIVEN NAME in reference 41 and 44 to non -abbreviation forms.