Deep Learning of EEG Signals for Brain Function and Injury: A Systematic Review

Abstract

Electroencephalography (EEG) is a valuable, low-cost tool for capturing brain signals, typically through non-invasive scalp recordings. However, analyzing EEG signals poses challenges due to noise interference and the need for specialized professionals. Recent advancements in deep learning have addressed many of these issues, leading to significant progress. Despite numerous systematic reviews, there remains a gap in deep learning research focused on assessing consciousness, brain injury, and comatose states. To bridge this gap, we conducted a systematic literature review following the PRISMA protocol, targeting human studies published between 2018 and 2023 across various databases. We identified 475 studies, of which 37 met our eligibility criteria. Their analyses revealed key trends: i) most focus on classification tasks, particularly for consciousness assessment and seizure detection; ii) most datasets involve fewer than 100 participants, with few being publicly available; iii) Convolutional Neural Networks dominate the learning architecture, although emerging approaches like Graph Attention Networks and Transformers show promising performance. However, challenges remain in distributed data acquisition, data augmentation, automated and multimodal machine learning, hyperparameter optimization, and model explainability, which are underexplored. In conclusion, while deep learning has advanced EEG signal analysis, further research is essential to build larger datasets and improve multimodal models for broader applications in Brain Function and Injury assessment.