PRISM: Deep metric learning based personal grouping method to reduce intersubject variability for motor imagery brain–computer interface

Abstract

Motor imagery brain–computer interfaces (MI–BCIs) that use electroencephalogram (EEG) signals are essential for motor rehabilitation. One of the most important problems in MI–BCIs is intersubject variability, as brain signals can vary significantly among individuals due to differences in cognitive processing and mental states. To address this issue, some studies have proposed intrasubject learning models that train and evaluate within specific individuals, while others have suggested intersubject learning models that aim to generalize across subjects. However, insufficient data are available for intrasubject learning, so they cannot use a deep learning model, and still, the problem of intersubject variability is unsolved in intersubject learning. To tackle these challenges, we developed a new framework based on deep metric learning called the Personal grouping method for Reducing InterSubject variability using deep Metric learning (PRISM). PRISM aims to reduce intersubject variability by calculating the similarity between subjects based on their EEG signals and generating clusters. Based on these clusters, the model can train by reducing intersubject variability while maintaining the advantage of intersubject learning. Our thorough evaluation indicates that PRISM is poised to redefine the standards in MI–BCIs, significantly alleviating the challenges of intersubject variability. We compared the performance of PRISM and the existing learning paradigm, and the performance of PRISM was similar to that of the intrasubject learning model (p > 0.4), which is the best existing model, and it performed better than the intersubject learning model (**p < 0.001). Our results indicate that the PRISM framework outperforms intersubject learning and closely matches intrasubject learning models. This suggests that PRISM combines the specificity of intrasubject learning with intersubject model generalizability, thereby enhancing performance.