Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals

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• 作者：Harsha Battapady ; Peter Lin ; Tom Holroyd ; Mark Hallett ; Xuedong Chen ; Ding-Yu Fei ; Ou Bai
• 关键词：Magnetoencephalography (MEG) ; Synthetic aperture magnetometry (SAM) ; Virtual channels ; Event-related desynchronization/synchronization (ERD/ERS) ; Brain– ; computer interface (BCI) ; Movement intention ; Motor control
• 刊名：Clinical Neurophysiology
• 出版年：2009
• 出版时间：November 2009
• 年：2009
• 卷：120
• 期：11
• 页码：1978-1987
• 全文大小：706 K

文摘

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 (15–30 Hz) 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 % ).

Conclusion

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

Significance

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.