Nested Deep Learning Model Towards A Foundation Model for Brain Signal Data

TL;DR

This paper introduces a Nested Deep Learning framework that enhances EEG/MEG spike detection by improving accuracy, adaptability to different channel configurations, and channel localization, aiding clinical diagnosis.

Contribution

The novel NDL model addresses limitations of existing methods by handling variable channel setups and enabling precise channel localization in brain signal analysis.

Findings

Improves spike detection accuracy on real EEG/MEG data

Supports cross-modality data integration

Enables accurate identification of key channels

Abstract

Epilepsy affects around 50 million people globally. Electroencephalography (EEG) or Magnetoencephalography (MEG) based spike detection plays a crucial role in diagnosis and treatment. Manual spike identification is time-consuming and requires specialized training that further limits the number of qualified professionals. To ease the difficulty, various algorithmic approaches have been developed. However, the existing methods face challenges in handling varying channel configurations and in identifying the specific channels where the spikes originate. A novel Nested Deep Learning (NDL) framework is proposed to overcome these limitations. NDL applies a weighted combination of signals across all channels, ensuring adaptability to different channel setups, and allows clinicians to identify key channels more accurately. Through theoretical analysis and empirical validation on real EEG/MEG datasets, NDL is shown to improve prediction accuracy, achieve channel localization, support cross-modality data integration, and adapt to various neurophysiological applications.