TY - GEN
T1 - Human motor cortical activity recorded with micro-ECoG electrodes during individual finger movements
AU - Wang, Wei
AU - Degenhart, Alan D.
AU - Collinger, Jennifer L.
AU - Vinjamuri, Ramana
AU - Sudre, Gustavo P.
AU - Adelson, P. David
AU - Holder, Deborah L.
AU - Leuthardt, Eric C.
AU - Moran, Daniel W.
AU - Boninger, Michael L.
AU - Schwartz, Andrew B.
AU - Crammond, Donald J.
AU - Tyler-Kabara, Elizabeth C.
AU - Weber, Douglas J.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77950982223&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2009.5333704
DO - 10.1109/IEMBS.2009.5333704
M3 - Conference contribution
C2 - 19964229
AN - SCOPUS:77950982223
SN - 9781424432967
T3 - Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
SP - 586
EP - 589
BT - Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - IEEE Computer Society
T2 - 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
Y2 - 2 September 2009 through 6 September 2009
ER -