EEGformer is a transformer-based method that can accurately classify brain activity from EEG signals for various applications such as early glaucoma diagnosis, emotion recognition, and depression discrimination.
EEGformer generalizes well to different EEG datasets, demonstrating our approach can be potentially suitable for providing accurate brain activity classification and being used in different application scenarios, such as SSVEP-based early glaucoma diagnosis, emotion recognition and depression discrimination.
Wan et al. (Fri,) studied this question.