Sleep Stage Classification across Heterogeneous EEG/EOG Channels with Multi-Channel Differential Transformer

arXiv:2508.15215 · cs.LG · Submitted 2025-08-21 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Sleep Stage Classification across Heterogeneous EEG/EOG Channels with Multi-Channel Differential Transformer".

Jane: Here's the gist of the paper. The gist The proposed SleepDIFFormer framework, a multi-channel differential transformer, learns domain-invariant EEG–EOG representations through cross-domain feature alignment to achieve superior sleep stage classification accuracy across diverse clinical configurations.

Tom: Problem and Motivation Classification of sleep stages is essential for assessing sleep quality and diagnosing sleep disorders <ref:2508.15215#pg2> Disruption of sleep patterns has been shown to degrade cognitive performance, particularly memory retention and attentional control, while increasing susceptibility to emotional instability <ref:2508.15215#pg3> Manual inspection of EEG characteristics for each stage is time-consuming and prone to human error <ref:2508.15215#pg4> EEG and EOG signals are inherently noisy due to their non-stationarity and high variability across domains (i.e., datasets) which results in poor generalization <ref:2508.15215#pg5> Clinical settings often result in significant distributional shifts across domains (i.e., datasets) and pose major challenges for generalization <ref:2508.15215#pg6> Transformer models have gained attention from researchers in language

11: , vision

12: , and time-series forecasting

13: for the capability of learning dependencies underlying time series segments after patching <ref:2508.15215#pg7> However, empirical studies have shown that performance improvements are marginal with increasing model scale, while the computational cost remains prohibitively high <ref:2508.15215#pg8> Moreover, zero-shot generalization performance has been found to be impractical.

Jane: Proposed Method: SleepDIFFormer Architecture SleepDIFFormer comprises a Multi-channel Differential Transformer Architecture (MDTA) combined with cross-domain feature alignment The MDTA is designed to process raw EEG and EOG signals while incorporating cross-domain alignment The framework is trained across multiple sleep staging datasets, each treated as a source domain, with the goal of generalizing to unseen target domains.

Tom: The MDTA includes specific components for signal processing and attention mechanisms: 1.

Title and authors: Tom: So, what does this paper actually propose? It introduces this SleepDIFFormer framework. Basically, they built a Multi-channel Differential Transformer Architecture combined with cross-domain feature alignment for heterogeneous EEG and EOG representation learning >

Jane: If we try to put that into simpler terms, it means they’re tackling the problem where sleep signals change depending on where you get them. They are training the model across many different datasets, treating each one as a source domain, so the goal is to make sure it can handle completely new datasets later on >

Lu: The MDTA part handles the signal processing and attention mechanisms. It has specific components like Signal Embedding, Differential Self-attention for intra-channel stuff, and Differential Cross-attention for inter-channel stuff >

Meng: From my side, I’m curious about how complex that architecture is going to be computationally. We're talking about these differential self and cross attentions >

Tom: Yeah, and they also have a Multi-Head Self-Attention after L layers of the MDTA to model inter-epoch dependencies. It uses concatenation and global average pooling instead of differential attention for that part >

The paper's summary: Jane: So, the big takeaway from this paper is that they are trying to solve two major problems at once: getting better classification accuracy across different clinical settings and dealing with the inherent noise and variability in EEG and EOG signals. They achieve this by using cross-domain feature alignment to learn representations that are invariant across those domains >

Tom: They specifically mention that the MDTA mitigates signal and attention noise, which is important because EEG and EOG signals are known for being very non-stationary >

Lu: And then they introduce this loss function called Ltotal, which includes classification loss, a reconstruction loss for the input signals, and a domain alignment loss called Lalign >

Meng: That alignment part sounds like the key to generalizing. They have these epoch-level losses and sequence-level losses that try to minimize statistical differences between source domains >

Jane: So, they’re not just training a classifier; they’re using those alignment losses, like the epoch-level loss Lexp which compares means and covariances across different datasets, to force the model to learn features that are consistent across those datasets >

The paper's improvements: Tom: One of the key improvements mentioned is how they handle noise. They use Differential Self-attention and Differential Cross-attention to mitigate signal and attention noise, and the ablation study showed that taking those parts out caused a performance drop >

Lu: That’s significant because it shows that those differential mechanisms are actually necessary for suppressing non-stationary noise in the signals >

Meng: I see what you mean. It's not just about having a Transformer; it's about making sure the attention mechanism isn't just learning spurious correlations specific to one dataset >

Jane: And they also address how to look at longer patterns. They use MHSA after L layers of the MDTA to capture inter-epoch dependencies, which helps them model how sleep stages evolve over longer time sequences >

Tom: So, they’re not just looking at a single moment in time; they are modeling the progression of sleep states across the whole recording >

Conclusion: Jane: So to wrap up on this paper, it’s about using a Multi-channel Differential Transformer for heterogeneous EEG and EOG signals to classify sleep stages, and they use cross-domain feature alignment to get those representations to be more generalizable across different clinical configurations >

Lu: The main implication is that this approach aims to enable stronger generalization for clinical utility, moving away from the impractical zero-shot performance they found in prior work >

Meng: For deployment, the focus should be on how stable these alignment losses are when you introduce new data streams at scale, because we need something robust for real traffic >

Lalam: I'm confident in this direction. The way it aligns epoch-level statistical properties like mean and covariance with sequence-level statistics using Pearson Correlation Coefficient really captures the temporal dependency structure across domains >

Tom: It’s a solid framework if those alignment losses actually work as intended to keep the representations domain invariant >

Jane: Well, that’s what we have for this paper. We'll be looking at how this SleepDIFFormer compares to other methods in the next segment.

Benjamin Wei Hao Chin, Yuin Torng Yew, Haocheng Wu, Lanxin Liang, Chow Khuen Chan, Norita Mohd Zain, Siti Balqis Samdin

Xiamen University Malaysia Department of Artificial Intelligence and Robotics Department of Biomedical Engineering Universiti Malaya Department of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia

cs.LG

Submitted: 2025-08-21

Updated: 2026-10-17

Comments: SleepDIFFormer 23 Pages. Accepted by Cognitive Neurodynamics, 2026. DOI: 10.1007/s11571-026-10574-5

DOI: 10.1007/s11571-026-10574-5

Code: https://github.com/Ben1001409/SleepDIFFormer

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 75/100

The gist: The gist The proposed SleepDIFFormer framework, a multi-channel differential transformer, learns domain-invariant EEG–EOG representations through cross-domain feature alignment to achieve superior

Key concepts

Multi-channel Differential Transformer (MDTA): This architecture processes EEG and EOG signals by embedding them into tokens and using differential self-attention for intra-channel interactions and differential cross-attention for inter-channel interactions. This helps the model capture complex spatial relationships between different channels while filtering out noise.
Cross-domain Feature Alignment (FA): This loss function explicitly minimizes the statistical differences between training datasets. It enforces alignment at epoch levels (mean and covariance) and sequence levels to ensure the learned representations are invariant to the specific clinical domain, promoting strong generalization.
Differential Self-attention (DSA): DSA is a mechanism used within the MDTA that compares different signal components (like EEG vs. EOG) within the same channel or modality. It helps in suppressing non-stationary noise by focusing on meaningful differences between signals rather than just raw signal values.
MHSA for Inter-Epoch Dependencies: Multi-Head Self-Attention (MHSA) with concatenation and global average pooling is used after several transformer layers to model long sequences. This allows the model to capture how sleep stages evolve over extended periods, such as across multiple nights or epochs.

Terminology

Generalization — 3× in this paper · explained in 14 episodes
Transformer — 3× in this paper · explained in 2 episodes
Ablation — 1× in this paper · explained in 1 episode
Ablation Study — 1× in this paper · explained in 3 episodes
Non-stationarity — 1× in this paper · explained in 2 episodes
Representation Learning — 1× in this paper · explained in 3 episodes
Transformer Architecture — 1× in this paper · explained in 4 episodes

Summary

The gist The proposed SleepDIFFormer framework, a multi-channel differential transformer, learns domain-invariant EEG–EOG representations through cross-domain feature alignment to achieve superior sleep stage classification accuracy across diverse clinical configurations.

Problem and Motivation

Classification of sleep stages is essential for assessing sleep quality and diagnosing sleep disorders <ref:2508.15215#pg2> Disruption of sleep patterns has been shown to degrade cognitive performance, particularly memory retention and attentional control, while increasing susceptibility to emotional instability <ref:2508.15215#pg3> Manual inspection of EEG characteristics for each stage is time-consuming and prone to human error <ref:2508.15215#pg4> EEG and EOG signals are inherently noisy due to their non-stationarity and high variability across domains (i.e., datasets) which results in poor generalization <ref:2508.15215#pg5> Clinical settings often result in significant distributional shifts across domains (i.e., datasets) and pose major challenges for generalization <ref:2508.15215#pg6> Transformer models have gained attention from researchers in language [11], vision [12], and time-series forecasting [13] for the capability of learning dependencies underlying time series segments after patching <ref:2508.15215#pg7> However, empirical studies have shown that performance improvements are marginal with increasing model scale, while the computational cost remains prohibitively high <ref:2508.15215#pg8> Moreover, zero-shot generalization performance has been found to be impractical.

Proposed Method: SleepDIFFormer Architecture

SleepDIFFormer comprises a Multi-channel Differential Transformer Architecture (MDTA) combined with cross-domain feature alignment The MDTA is designed to process raw EEG and EOG signals while incorporating cross-domain alignment The framework is trained across multiple sleep staging datasets, each treated as a source domain, with the goal of generalizing to unseen target domains.

The MDTA includes specific components for signal processing and attention mechanisms:

Signal Embedding (SE): A hierarchical convolutional module is designed to convert raw signals into tokens <ref:2508.15215#pg3> After passing through the phi(·), a set of N series tokens were obtained, with sn ∈ R d <ref:2508.15215#pg4> These resulting tokens were further enriched by adding learnable positional embeddings (PE), applied separately to modality m, which is EEG or EOG <ref:2508.15215#pg5>.
Differential Self-attention (DSA) for Intra-Channel: This mechanism is defined as S′m, G′m = DSA([Sm, Gm]) <ref:2508.15215#pg7> The concatenated series tokens Sm and global tokens Gm serves as the shared input to construct Q, K, V for differential self-attention module <ref:2508.15215#pg8>.
Differential Cross-attention (DCA) for Inter-Channel: To capture multi-channel representations and interactions, DA is incorporated into cross-attention mechanism between the global tokens of EEG and EOG The DCA is defined as G′′′eeg = LayerNorm G′′ eeg + DCA(G′′ eeg, G'eog).
MHSA for Inter-Epoch Dependencies: After L layers of MDTA, MHSA was employed to model sequence-level context and enhance the model’s ability to capture the evolution of sleep stages over long periods For inter-epoch dependencies, MHSA with concatenation (Concat) and global average pooling (AvgPool) was utilized instead of DA.

Loss Function for Generalization

The total loss function is defined as Ltotal = Lcls + λrecLrec + λalign (Lepo + Lseq) The classification loss is the cross-entropy loss, where PredSDi n,c denotes the predicted probability and y SDi n,c is the annotation for the n-th sample and c-th class from domain SDi. The reconstruction loss Lrec = 1/T Σ X T t=1 xt − xˆt squared is used for signal reconstruction of inputs.

To enhance cross-domain generalization, a multi-level feature alignment (FA) loss is incorporated to explicitly minimize distributional difference between source domains This consists of epoch-level alignment losses:

** Epoch-level loss Lexp = Σ i!=j E(S¯i) − E(S¯j) / 2 squared and covariance loss Lcov = Σ i!=j COV(S¯i) − COV(S¯j) / 2 F. These losses enforce local distribution alignment across domains. Additionally, sequence-level alignment is adopted: Lseq = Σ i!=j Ri − Rj squared F. The overall loss function is Ltotal = Lcls + λrecLrec + λalign (Lepo + Lseq). The parameters λrec and λalign control the relative importance of reconstruction and domain alignment. By aligning epoch-level statistical properties, mean [44] and covariance [45], together with sequence-level [10] statistical properties, the model was encouraged to learn domaininvariant representations while preserving temporal structures. The correlation matrices Ri and Rj are computed using the Pearson Correlation Coefficient (PCC) from the sleep sequences in each domain SDi and SDj. This component captured and aligned the temporal dependency structure across domains at the sequence level. The overall loss function is Ltotal = Lcls + λrecLrec + λalign (Lepo + Lseq). The parameters λrec and λalign control the relative importance of reconstruction and domain alignment. By aligning epoch-level statistical properties, mean [44] and covariance [45], together with sequence-level [10] statistical properties, the model was encouraged to learn domaininvariant representations while preserving temporal structures. The correlation matrices Ri and Rj are computed using the Pearson Correlation Coefficient (PCC) from the sleep sequences in each domain SDi and SDj. This component captured and aligned the temporal dependency structure across domains at the sequence level. The overall loss function is Ltotal = Lcls + λrecLrec + λalign (Lepo + Lseq). The parameters λrec and λalign control the relative importance of reconstruction and domain alignment. By aligning epoch-level statistical properties, mean [44] and covariance [45], together with sequence-level [10] statistical properties, the model was encouraged to learn domaininvariant representations while preserving temporal structures. The correlation matrices Ri and Rj are computed using the Pearson Correlation Coefficient (PCC) from the sleep sequences in each domain SDi and SDj. This component captured and aligned the temporal dependency structure across domains at the sequence level. The overall loss function is Ltotal = Lcls + λrecLrec + λalign (Lepo + Lseq). The parameters λrec and λalign control the relative importance of reconstruction and domain alignment. By aligning epoch-level statistical properties, mean [44] and covariance [45], together with sequence-level [10] statistical properties, the model was encouraged to learn domaininvariant representations while preserving temporal structures. The correlation matrices Ri and Rj are computed using the Pearson Correlation Coefficient (PCC) from the sleep sequences in each domain SDi and SDj. This component captured and aligned the temporal dependency structure across domains at the sequence level. The overall loss function is Ltotal = Lcls + λrecLrec + λalign (Lepo + Lseq). The parameters λrec and λalign control the relative importance of reconstruction and domain alignment. By aligning epoch-level statistical properties, mean [44] and covariance [45], together with sequence-level [10] statistical properties, the model was encouraged to learn domaininvariant representations while preserving temporal structures. The correlation matrices Ri and Rj are computed using the Pearson Correlation Coefficient (PCC) from the sleep sequences in each domain SDi and SDj. This component captured and aligned the temporal dependency structure across domains at the sequence level. The overall loss function is Ltotal = Lcls + λrecLrec + λalign (Lepo + Lseq). The parameters λrec and λalign control the relative importance of reconstruction and domain alignment. By aligning epoch-level statistical properties, mean [44] and covariance [45], together with sequence-level [10] statistical properties, the model was encouraged to learn domaininvariant representations while preserving temporal structures.

Improvements for AI systems

Multi-channel differential transformer for cross-domain sleep stage classification allows AI systems to classify sleep stages using heterogeneous EEG and EOG signals across diverse clinical configurations, addressing poor generalization due to non-stationarity.
The model can learn domain invariant EEG–EOG representations through feature distribution alignment across datasets, enabling robust performance on unseen target domains without requiring further training or fine-tuning.
The Multi-channel Differential Transformer Architecture (MDTA) mitigates spatial and temporal attention noise by employing differential self-attention (DSA) and differential cross-attention (DCA), enhancing the sparsity and selectivity of the attention distribution.
AI systems can capture hierarchical spatiotemporal dependencies of EEG and EOG signals by utilizing MDTA, learning hierarchical spatiotemporal dependencies of EEG and EOG to model complex physiological patterns.
The system can effectively suppress non-stationary noise by using DSA and DCA, as demonstrated in the ablation study showing that removing these components resulted in a performance drop.
The framework can capture inter-epoch dependencies by utilizing MHSA with concatenation and global average pooling for sequence-level context, which enhances the model’s ability to capture the evolution of sleep stages over long periods.
The system can provide interpretable predictions by visualizing differential attention weights, allowing researchers to examine whether the model’s decisions aligned with established sleep science by highlighting stage-specific features like delta waves or REM characteristics.