Unimanual Versus Bimanual Motor Imagery Classifiers for Assistive and Rehabilitative Brain Computer Interfaces

摘要

Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet, electroencephalography (EEG)-based assistive and rehabilitative brain–computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time. In this paper, we present a classifier which discriminates between uni-and bi-manual MI. Ten able-bodied participants took part in cue-based motor execution (ME) and MI tasks of the left (L), right (R) and both (B) hands. A 32-channel EEG was recorded. Three linear discriminant analysis classifiers, based on MI of L–B, B–R, and B–L hands were created, with features based on wide band common spatial patterns (CSP) 8–30 Hz, and band specifics common spatial patterns (CSPb). Event-related desynchronization (ERD) was significantly stronger during bimanual compared to unimanual ME on both hemispheres. Bimanual MI resulted in bilateral parietally shifted ERD of similar intensity to unimanual MI. The average classification accuracy for CSP and CSPb was comparable for the L–R task (73% ± 9% and 75% ± 10%, respectively) and for the L–B task (73% ± 11% and 70% ± 9%, respectively). However, for the R–B task (67% ± 3% and 72% ± 6%, respectively), it was significantly higher for CSPb ($p = 0.0351$). Six participants whose L–R classification accuracy exceeded 70% were included in an online task a week later, using the unmodified offline CSPb classifier, achieving 69% ± 3% and 66% ± 3% accuracy for L–R and R–B tasks, respectively. Combined uni- and bi-manual BCI could be used for restoration of motor function of highly disabled patents and for motor rehabilitation of patients with motor deficits.