Brain-computer interfaces (BCIs) face challenges due to variability in EEG data across different subjects, requiring frequent calibration for each new user or session.
A new approach using domain generalization aims to create an EEG classification framework that works with data from unknown subjects based on previously collected data from other individuals.
The proposed framework utilizes open-set recognition to enhance feature extraction and improve generalization, potentially reducing the need for calibration and broadening applications in mental state monitoring tasks.