Wang W, Degenhart A D, Collinger J L, Vinjamuri R, Sudre G P, Adelson P D, Holder D L, Leuthardt E C, Moran D W, Boninger M L, Schwartz A B, Crammond D J, Tyler-Kabara E C, Weber D J
University of Pittsburgh, Pittsburgh, PA, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:586-9. doi: 10.1109/IEMBS.2009.5333704.
In this study human motor cortical activity was recorded with a customized micro-ECoG grid during individual finger movements. The quality of the recorded neural signals was characterized in the frequency domain from three different perspectives: (1) coherence between neural signals recorded from different electrodes, (2) modulation of neural signals by finger movement, and (3) accuracy of finger movement decoding. It was found that, for the high frequency band (60-120 Hz), coherence between neighboring micro-ECoG electrodes was 0.3. In addition, the high frequency band showed significant modulation by finger movement both temporally and spatially, and a classification accuracy of 73% (chance level: 20%) was achieved for individual finger movement using neural signals recorded from the micro-ECoG grid. These results suggest that the micro-ECoG grid presented here offers sufficient spatial and temporal resolution for the development of minimally-invasive brain-computer interface applications.
在本研究中，在个体手指运动期间使用定制的微电极脑电图（micro-ECoG）网格记录人类运动皮层活动。从三个不同角度在频域中对记录的神经信号质量进行了表征：（1）从不同电极记录的神经信号之间的相干性，（2）手指运动对神经信号的调制，以及（3）手指运动解码的准确性。结果发现，对于高频带（60 - 120赫兹），相邻微电极脑电图电极之间的相干性为0.3。此外，高频带在时间和空间上均显示出由手指运动引起的显著调制，并且使用从微电极脑电图网格记录的神经信号对个体手指运动实现了73%的分类准确率（机遇水平：20%）。这些结果表明，此处展示的微电极脑电图网格为微创脑机接口应用的开发提供了足够的空间和时间分辨率。
