Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements
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Date
2009-11-13
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Citation of Original Publication
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.; Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements; 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2009); https://ieeexplore.ieee.org/document/5333704
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© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Abstract
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.