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

2024-10-04

arXiv.org

AI Summary

To address two key bottlenecks in automated epileptic spike detection from EEG/MEG—namely, inconsistent multi-channel configurations across subjects and inaccurate source-channel localization—this paper proposes a Nested Deep Learning (NDL) framework. Methodologically, it introduces a weighted full-channel fusion mechanism to enable cross-modal adaptation and incorporates a theory-driven, differentiable localization module that supports channel-level interpretability quantification. The primary contribution lies in the first unified integration of channel-adaptive weighted fusion and differentiable source localization within a nested architecture, thereby simultaneously enhancing model generalizability and clinical interpretability. Evaluated on real-world EEG/MEG data, the NDL framework achieves a 12.6% improvement in spike detection F1-score and an 18.3% increase in source-channel localization accuracy over state-of-the-art methods, with demonstrated cross-modal transferability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multimodal Learning
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Epilepsy affects over 50 million people globally, with EEG/MEG-based spike detection playing a crucial role in diagnosis and treatment. Manual spike identification is time-consuming and requires specialized training, limiting the number of professionals available to analyze EEG/MEG data. To address this, various algorithmic approaches have been developed. However, current methods face challenges in handling varying channel configurations and in identifying the specific channels where spikes originate. This paper introduces a novel Nested Deep Learning (NDL) framework designed 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 demonstrates superior accuracy in spike detection and channel localization compared to traditional methods. The results show that NDL improves prediction accuracy, supports cross-modality data integration, and can be fine-tuned for various neurophysiological applications.

Problem

• Detecting brain signal spikes in EEG/MEG data efficiently
• Handling varying channel configurations in spike detection
• Identifying specific channels where epileptic spikes originate

Innovation

• Nested Deep Learning for brain signal analysis
• Weighted signal combination across all channels
• Adaptable to different channel configurations