Mentality: A Mamba-based Approach towards Foundation Models for EEG
This is an incremental step toward clinically applicable foundation models for EEG analysis, potentially aiding in diagnosing conditions like epilepsy.
This work tackled the problem of analyzing noisy and complex EEG data for neurological disorder diagnosis by developing a Mamba-based foundation model, achieving an AUROC of 0.72 on a seizure detection task.
This work explores the potential of foundation models, specifically a Mamba-based selective state space model, for enhancing EEG analysis in neurological disorder diagnosis. EEG, crucial for diagnosing conditions like epilepsy, presents significant challenges due to its noisy, high-dimensional, and nonlinear nature. Traditional machine learning methods have made advances in automating EEG analysis but often fail to capture its complex spatio-temporal dynamics. Recent advances in deep learning, particularly in sequence modeling, offer new avenues for creating more generalized and expressive models capable of handling such complexities. By training a Mamba-based model on a large dataset containing seizure and non-seizure EEG recordings through a self-supervised reconstruction task followed by a seizure detection task, we demonstrate the model's effectiveness, achieving an AUROC of 0.72 on a held-out test set. This approach marks a significant step toward developing large-scale, clinically applicable foundation models for EEG data analysis.