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

Area of Science:

• Neuroscience
• Artificial Intelligence
• Medical Diagnostics

Background:

• Neurological disorders present significant global health challenges.
• Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are crucial for diagnosing and monitoring neurological conditions.
• Dataset heterogeneity and task variability impede robust deep learning model development for brain signal analysis.

Purpose of the Study:

• To systematically review recent deep learning advancements for EEG/iEEG-based neurological diagnostics.
• To analyze methods, performance, data usage, and model designs across 7 neurological conditions and 46 datasets.
• To highlight the potential of pre-trained multi-task models for scalable and generalizable solutions.

Main Methods:

• Systematic literature review of deep learning approaches for neurological diagnostics.
• Analysis of 46 datasets covering 7 distinct neurological conditions.
• Integration of performance comparisons with data usage, model design, and task-specific adaptations.

Main Results:

• Identified key deep learning methods and their quantitative results for various neurological conditions.
• Demonstrated the role of pre-trained multi-task models in enhancing scalability and generalizability.
• Highlighted the impact of recent innovations on intelligent and adaptable neurological healthcare systems.

Conclusions:

• Deep learning shows significant promise for improving neurological diagnostics using EEG/iEEG data.
• Standardized benchmarks are essential for evaluating model performance and enhancing reproducibility.
• Future research should focus on developing adaptable and scalable deep learning solutions for diverse neurological conditions.