A Non-Invasive, MRCP-Based BCI for Online Communication

Patients with severely impaired motor functions require a stable form of communication for their daily life. Restoring this ability can be achieved with spelling applications controlled by brain-computer interfaces (BCIs). To achieve intuitive control of the application, we propose a BCI system to asynchronously detect single movement intent from EEG. By emulating a button press, we develop a task-agnostic framework applicable to a wide range of interfaces. The system utilizes a model based on movement-related cortical potentials (MRCPs) to detect self-initiated movements without the need for external cues. Twenty participants utilized the developed system to control a spelling interface implemented as a row-column scanner (3-by-3 and 5-by-5 size layouts) to type five-letter words. Participants achieved an overall true positive rate (TPR) of $54.4 \pm 27.9{\%}$ (up to 98.6% in single participants) with an average of $2.0 \pm 1.9$ false positives per minute (FP/min). $60.9 \pm 28.5{\%}$ of the target characters were correctly selected and participants were able to successfully spell a five-letter word in $41.7 \pm 42.7 {\%}$ of all attempts. The analysis of the EEG showed that the MRCP-based classifier maintained consistent detection performance across interface configurations, underscoring its robustness and adaptability to changing applications. These findings demonstrate the potential of the approach as a non-invasive communication aid and establish a foundation for future development of home-use BCIs that offer intuitive, voluntary control with minimal calibration requirements.