Deep learning architectures, including CNNs, RNNs, and Transformers, improve EEG-based healthcare applications by automating feature extraction and enhancing diagnostic accuracy across various conditions.
This review summarizes the application of deep learning models to EEG signal analysis for neurological diagnostics, brain recovery, mental health, and BCI applications.
Electroencephalography (EEG) is a longstanding means of non-invasively recording brain signals and has become highly valuable for the study of neurological and cognitive processes. Recent progress in deep learning has also greatly improved both EEG signal analysis and interpretation, making more accurate, reliable and scalable solutions in various healthcare applications. In this review, we present a comprehensive summary of the convergence of EEG and deep learning, with an emphasis on diagnostic of neurological disorders, brain recovery, mental health conditions, and brain-computer interface (BCI) applications. We methodically investigate the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, transformer models and hybrid architectures for EEG-based tasks. Key challenges that have been hampering emerging solutions are critically covered, namely signal-related variability, the lack of data, and deep learning model limited interpretability. Finally, we highlight emerging trends, open issues and promising research directions, with the aim of laying a solid ground toward the improvement of EEG-based healthcare applications and to drive future research in this fast-growing research area.
RuiFang Lyu (Fri,) conducted a review in Neurological disorders, sleep disorders, mental health conditions, and brain-computer interface applications. Deep learning approaches (CNNs, RNNs, LSTM, Transformers, GNNs) vs. Traditional signal processing and machine learning methods was evaluated. Deep learning architectures, including CNNs, RNNs, and Transformers, improve EEG-based healthcare applications by automating feature extraction and enhancing diagnostic accuracy across various conditions.