Subject Selection Framework to Improve Personalised Models for Motor-Imagery BCIs via Wavelets and Graph Diffusion

Personalized electroencephalogram (EEG) decoders hold a distinct preference in healthcare applications, especially in the context of Motor-Imagery (MI) Brain-Computer Interfaces (BCIs), owing to their inherent capability to effectively tackle inter-subject variability. This study introduces a novel subject selection framework that blends ideas from discriminative learning (based on continuous wavelet transform) and graph-signal processing (over the sensor array). Through experimentation with a publicly available MI dataset, we showcase enhanced personalized performance for MI-BCIs. Notably, it proves particularly advantageous for subjects who initially demonstrated suboptimal personalized performance.