Survey on Deep Learning for EEG Decoding Challenges

This survey addresses the challenges in cross-subject EEG decoding due to high inter-subject variability, which complicates the domain shift between training and unseen test subjects. It outlines deep learning methodologies, formulating the problem within a multi-source domain framework and establishing subject-independent evaluation protocols necessary for validation. A systematic taxonomy of the literature categorizes existing approaches into families such as feature alignment and adversarial learning.

The implications of this research are significant for advancing EEG technology, emphasizing critical areas for further development, including understanding theoretical limitations of current methodologies and recognizing the structural value of subject identity. The emergence of EEG foundation models highlights the potential for improved robustness in decoding applications, suggesting a pathway towards more effective real-world implementations in healthcare and beyond.