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

Jiahe Li, Xin Chen, Fanqi Shen, Junru Chen, Yuxin Liu, Daoze Zhang, Zhizhang Yuan, Fang Zhao, Meng Li, Yang Yang

TL;DR

This survey addresses how deep learning can advance EEG/iEEG-based neurological diagnostics amidst substantial data heterogeneity and task diversity. It highlights self-supervised, multi-task pretraining as a promising route to universal representations, and introduces the BrainBenchmark platform to standardize evaluation across datasets and models. It synthesizes 46 public datasets spanning seven conditions and delineates task formulations, data handling, and architectural trends from CNNs to Transformers and graph networks. Collectively, the work delineates a path toward scalable, adaptable diagnostic systems that generalize across patients and datasets, with potential to transform clinical practice. The establishment of BrainBenchmark and emphasis on SSL-driven universality position EEG/iEEG diagnostics for robust, cross-domain deployment in diverse healthcare settings.

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.

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

TL;DR

This survey addresses how deep learning can advance EEG/iEEG-based neurological diagnostics amidst substantial data heterogeneity and task diversity. It highlights self-supervised, multi-task pretraining as a promising route to universal representations, and introduces the BrainBenchmark platform to standardize evaluation across datasets and models. It synthesizes 46 public datasets spanning seven conditions and delineates task formulations, data handling, and architectural trends from CNNs to Transformers and graph networks. Collectively, the work delineates a path toward scalable, adaptable diagnostic systems that generalize across patients and datasets, with potential to transform clinical practice. The establishment of BrainBenchmark and emphasis on SSL-driven universality position EEG/iEEG diagnostics for robust, cross-domain deployment in diverse healthcare settings.

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.

Paper Structure

This paper contains 53 sections, 2 equations, 1 figure, 36 tables.

Table of Contents

  1. Introduction
  2. General Workflow
  3. Related Studies and Our Contributions
  4. Methods
  5. Problem Definition
  6. Sample-Level Classification
  7. Event-Level Classification
  8. Signal Collection
  9. Signal Preprocessing
  10. Feature Extraction
  11. Data Partitioning Strategies
  12. Deep Learning Architectures
  13. Deep Learning Paradigms
  14. Supervised Learning
  15. Unsupervised Learning
  16. ...and 38 more sections

Figures (1)

  • Figure 1: General Workflow of Electrical Brain Signals Analysis in Neurological Diagnostics. a. Signal Collection: Acquisition of EEG/iEEG signals from patients, capturing brain electrical activity for clinical purposes. b. Signal Preprocessing: A feasible workflow to process raw signals, ensuring their suitability for subsequent analysis. c. Analysis and Diagnosis: Feature extraction and deep learning-based training for neurological classification. d. Statistical Information: Statistical summary of resources for neurological conditions, including related work and datasets.