Battapady Harsha, Lin Peter, Fei Ding-Yu, Huang Dandan, Bai Ou
Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:524-7. doi: 10.1109/IEMBS.2009.5333632.
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.
本研究的目的是探索能否通过可靠地解码与维持或停止右手和左手运动相关的单次试验脑磁图（MEG）信号来实现二维脑机接口（BCI）。七名未经训练的受试者参与了该研究。信号由275通道MEG记录，并采用了合成孔径磁测量法（SAM）。使用直接决策树分类器和基于遗传算法的马氏线性距离，通过10折交叉验证对用于四向控制的多类分类进行了离线评估。在与右手和左手运动相关的运动区域中，观察到了与实际运动以及运动想象相关的β波段（15 - 30Hz）事件相关去同步化和事件相关同步化。从SAM滤波后的MEG信号进行的四向分类的交叉验证准确率，对于实际运动高达95 - 97%，对于运动想象为86 - 87%。高分类准确率表明，从SAM滤波后的MEG信号对人类自然运动意图进行单次试验检测，可以实现可靠的高性能二维BCI，用户可能无需大量训练。
