Status of deep learning for EEG-based brain–computer interface applications - Hossain, Khondoker Murad; Islam, Md. Ariful; Hossain, Shahera; Nijholt, Anton; Ahad, Md Atiqur Rahman (Frontiers in Computational Neuroscience, 2023)
In the previous decade, breakthroughs in central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and brain-injured patients (e.g., stroke patients). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. But things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks like motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a paper that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this work on the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences—merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future work. We argue that this review paper will help the EEG research community in their future research.