Optimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approach
arXiv:2604.01595v1 Announce Type: new Abstract: Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter-patient variability. To model them, recent methods construct dynamic graphs via statistical correlations, predefined similarity measures, or implicit learning, yet rarely account for EEG's noisy nature. Consequently, these graphs usually contain redundant or task-irrelevant connections, undermining model performance even with state-of-the-art architectures. In this paper, we present a new perspective for EEG seizure detection: jointly learning denoised dynamic graph structures and informative spatial-temporal representations guided by the Information Bottleneck (IB). Unlike prior approaches, our graph constructor explicitly accounts for the noisy characteristics of EEG data, producing compact and reliable connectivity patterns that better support downstream seizure detection. To further enhance representation learning, we emp
arXiv:2604.01595v1 Announce Type: new Abstract: Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter-patient variability. To model them, recent methods construct dynamic graphs via statistical correlations, predefined similarity measures, or implicit learning, yet rarely account for EEG's noisy nature. Consequently, these graphs usually contain redundant or task-irrelevant connections, undermining model performance even with state-of-the-art architectures. In this paper, we present a new perspective for EEG seizure detection: jointly learning denoised dynamic graph structures and informative spatial-temporal representations guided by the Information Bottleneck (IB). Unlike prior approaches, our graph constructor explicitly accounts for the noisy characteristics of EEG data, producing compact and reliable connectivity patterns that better support downstream seizure detection. To further enhance representation learning, we employ a self-supervised Graph Masked AutoEncoder that reconstructs masked EEG signals based on dynamic graph context, promoting structure-aware and compact representations aligned with the IB principle. Bringing things together, we introduce Information Bottleneck-guided EEG SeizuRE DetectioN via SElf-Supervised Learning (IRENE), which explicitly learns dynamic graph structures and interpretable spatial-temporal EEG representations. IRENE addresses three core challenges: (i) Identifying the most informative nodes and edges; (ii) Explaining seizure propagation in the brain network; and (iii) Enhancing robustness against label scarcity and inter-patient variability. Extensive experiments on benchmark EEG datasets demonstrate that our method outperforms state-of-the-art baselines in seizure detection and provides clinically meaningful insights into seizure dynamics. The source code is available at https://github.com/LabRAI/IRENE.
Executive Summary
This paper presents a novel approach to EEG seizure detection, leveraging the Information Bottleneck (IB) principle to optimize dynamic graph structure and self-supervised learning. The proposed method, IRENE, overcomes challenges in EEG signal analysis, including noise, redundancy, and task-irrelevance, by explicitly learning compact and reliable connectivity patterns. The approach is evaluated on benchmark datasets, demonstrating superior performance to state-of-the-art baselines. Notably, IRENE provides clinically meaningful insights into seizure dynamics, addressing key challenges in EEG analysis. The method's strengths lie in its ability to identify informative nodes and edges, explain seizure propagation, and enhance robustness against label scarcity and inter-patient variability. The paper's findings have significant implications for seizure detection and neurological research, highlighting the potential of IB-guided approaches in biomedical signal processing.
Key Points
- ▸ IRENE leverages the Information Bottleneck (IB) principle to optimize dynamic graph structure for EEG seizure detection
- ▸ The approach employs self-supervised Graph Masked AutoEncoder to enhance representation learning
- ▸ IRENE outperforms state-of-the-art baselines in seizure detection and provides clinically meaningful insights
Merits
Strength in Addressing EEG Challenges
IRENE explicitly accounts for EEG's noisy nature, producing compact and reliable connectivity patterns, and enhances robustness against label scarcity and inter-patient variability.
Demerits
Limitation in Generalizability
IRENE's performance may be specific to EEG seizure detection and may not generalize to other biomedical applications.
Expert Commentary
IRENE's innovative approach to EEG seizure detection, grounded in the Information Bottleneck principle, marks a significant advancement in the field. By addressing key challenges in EEG analysis, the method demonstrates exceptional potential for clinical application and has far-reaching implications for biomedical research. Notably, the paper's focus on interpretable spatial-temporal representations and compact connectivity patterns highlights the importance of structure-aware learning in signal processing. As the field continues to evolve, it is essential to explore the broader implications of IB-guided approaches, including their potential applications in other biomedical domains.
Recommendations
- ✓ Future research should investigate the generalizability of IRENE to other biomedical applications and explore its potential in other signal processing domains
- ✓ The development of open-source software and publicly available datasets will facilitate the adoption and extension of IRENE, promoting collaboration and innovation in the field
Sources
Original: arXiv - cs.LG
