Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals.
OBJECTIVE
To test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single-trial magnetoencephalographic (MEG) signals for motor execution and motor imagery.


METHODS
Seven healthy volunteers, naïve to BCI technology, participated in this study. Signals were recorded from 275-channel MEG, and synthetic aperture magnetometry (SAM) was employed as the spatial filter. The four-class classification was performed offline. Genetic algorithm based Mahalanobis linear distance (GA-MLD) and direct-decision tree classifier (DTC) techniques were adopted for the classification through 10-fold cross-validation.


RESULTS
Through SAM imaging, strong and distinct event-related desynchronization (ERD) associated with sustaining, and event-related synchronization (ERS) patterns associated with ceasing of right and left hand movements were observed in the beta band (15-30Hz) on the contralateral hemispheres for motor execution and motor imagery sessions. Virtual channels were selected from these areas of high activity for the corresponding events as per the paradigm of the study. Through a statistical comparison between SAM-filtered virtual channels from single-trial MEG signals and basic MEG sensors, it was found that SAM-filtered virtual channels significantly increased the classification accuracy for motor execution (GA-MLD: 96.51+/-2.43%) as well as motor imagery sessions (GA-MLD: 89.69+/-3.34%).


CONCLUSIONS
Multiple movement intentions can be reliably detected from SAM-based spatially filtered single-trial MEG signals.


CONCLUSIONS
MEG signals associated with natural motor behavior may be utilized for a reliable high-performance brain-computer interface (BCI) and may reduce long-term training compared with conventional BCI methods using rhythm control.
Battapady
Harsha
H
EEG & BCI Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA.
Lin
Peter
P
Human Motor Control Section, Medical Neurological Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
Holroyd
Tom
T
MEG Core Facility, National Institute of Mental Health, Bethesda, MD 20892, USA.
Hallett
Mark
M
Human Motor Control Section, Medical Neurological Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
Chen
Xuedong
X
State Key Laboratory of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
Fei
Ding-Yu
DY
EEG & BCI Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA.
Bai
Ou
O
EEG & BCI Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA; Human Motor Control Section, Medical Neurological Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA. Electronic address: obai@vcu.edu.
D000328:Adult; D005071:Evoked Potentials / Q000502:physiology; D005260:Female; D006801:Humans; D008280:Magnetics / Q000379:methods*; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D009043:Motor Activity / Q000502:physiology; D009068:Movement / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods; D055815:Young Adult