Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics

TL;DR

This paper reviews recent deep learning methods applied to EEG and iEEG data for diagnosing neurological disorders, highlighting advances, challenges, and proposing a standardized benchmark for evaluation.

Contribution

It systematically analyzes recent deep learning approaches across multiple neurological conditions and datasets, emphasizing the role of pre-trained models and proposing a benchmark for reproducibility.

Findings

Deep learning models improve diagnostic accuracy for neurological conditions.

Pre-trained multi-task models enhance scalability and generalization.

A standardized benchmark facilitates fair comparison and reproducibility.

Abstract

Neurological disorders pose major global health challenges, driving advances in brain signal analysis. Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are widely used for diagnosis and monitoring. However, dataset heterogeneity and task variations hinder the development of robust deep learning solutions. This review systematically examines recent advances in deep learning approaches for EEG/iEEG-based neurological diagnostics, focusing on applications across 7 neurological conditions using 46 datasets. For each condition, we review representative methods and their quantitative results, integrating performance comparisons with analyses of data usage, model design, and task-specific adaptations, while highlighting the role of pre-trained multi-task models in achieving scalable, generalizable solutions. Finally, we propose a standardized benchmark to evaluate models across diverse datasets and improve reproducibility, emphasizing how recent innovations are transforming neurological diagnostics toward intelligent, adaptable healthcare systems.