Single trial detection of human movement intentions from SAM-filtered MEG signals for a high performance two-dimensional BCI.
The objective of this research is to explore whether a two-dimensional BCI can be achieved by reliably decoding single-trial magneto-encephalography (MEG) signal associated with sustaining or ceasing right and left hand movements. Seven naïve subjects participated in the study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed. The multi-class classification for four-directional control was evaluated offline from 10-fold cross-validation using direct-decision tree classifier and genetic algorithm based Mahalanobis linear distance. Beta band (15-30Hz) event-related desynchronization and event related synchronization were observed in right and left hand movement related motor areas for physical movements as well as motor imagery. The cross-validation accuracy for the proposed four-direction classification from SAM- filtered MEG signal was as high as 95-97% for physical movements and 86-87% for motor imagery. The high classification accuracy suggests that a reliable high performance two-dimensional BCI can be achieved from single trial detection of human natural movement intentions from SAM-filtered MEG signals, where user may not need extensive training.
Battapady
Harsha
H
Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA.
Lin
Peter
P
Fei
Ding-Yu
DY
Huang
Dandan
D
Bai
Ou
O
D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005260:Female; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface; D014836:Volition / Q000502:physiology*