Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics
Journal:
arXiv
Published Date:
Feb 24, 2025
Abstract
Neurological disorders represent significant global health challenges,
driving the advancement of brain signal analysis methods. Scalp
electroencephalography (EEG) and intracranial electroencephalography (iEEG) are
widely used to diagnose and monitor neurological conditions. However, dataset
heterogeneity and task variations pose challenges in developing 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. We explore
trends in data utilization, model design, and task-specific adaptations,
highlighting the importance of pre-trained multi-task models for scalable,
generalizable solutions. To advance research, we propose a standardized
benchmark for evaluating models across diverse datasets to enhance
reproducibility. This survey emphasizes how recent innovations can transform
neurological diagnostics and enable the development of intelligent, adaptable
healthcare solutions.
