
The inherent differences in brainwave patterns across different individuals present a persistent obstacle for the development of universal brain-computer interface systems. This phenomenon, often referred to as inter-subject variability, means that a machine learning model trained on one person’s neural data frequently fails when applied to another, necessitating tedious daily calibration sessions. Researchers have long sought a “plug-and-play” solution that










