Signal Processing for Brain–Computer Interfaces: A review and current perspectives

Brain–computer interfaces (BCIs) employ neurophysiological signals derived from the brain to control computers or external devices. By enhancing replacing human peripheral functioning capacity, BCIs offer supplementary degrees of freedom, significantly improving individuals’ quality life, particularly offering hope for those with locked-in syndrome (LIS). Moreover, BCI applications have expanded across medical and nonmedical domains, including rehabilitation, clinical diagnosis, cognitive affective computing, gaming. Over past decades, a wealth captured invasively noninvasively, has made spectacular progress. However, this also poses new challenges signal processing techniques, such as characterization classification. In review, we first introduce enhancement methods mine inherent patterns nonstationary time-varying signals. Then, highlight widely adopted classification in they face. This article aims comprehensively overview crucial techniques provide suggestions future directions.