NDLI: Classification of multichannel ECoG related to individual finger movements with redundant spatial projections

Content Provider	IEEE Xplore Digital Library
Author	Onaran, I. Ince, N.F. Cetin, A.E.
Copyright Year	2011
Description	Author affiliation: Electrical and Computer Engineering Department, University of Minnesota, Minneapolis, MN 55455 USA (Ince, N.F.) \|\| Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey (Onaran, I.; Cetin, A.E.)
Abstract	We tackle the problem of classifying multichannel electrocorticogram (ECoG) related to individual finger movements for a brain machine interface (BMI). For this particular aim we applied a recently developed hierarchical spatial projection framework of neural activity for feature extraction from ECoG. The algorithm extends the binary common spatial patterns algorithm to multiclass problem by constructing a redundant set of spatial projections that are tuned for paired and group-wise discrimination of finger movements. The groupings were constructed by merging the data of adjacent fingers and contrasting them to the rest, such as the first two fingers (thumb and index) vs. the others (middle, ring and little). We applied this framework to the BCI competition IV ECoG data recorded from three subjects. We observed that the maximum classification accuracy was obtained from the gamma frequency band (65200Hz). For this particular frequency range the average classification accuracy over three subjects was 86.3%. These results indicate that the redundant spatial projection framework can be used successfully in decoding finger movements from ECoG for BMI.
Starting Page	5424
Ending Page	5427
File Size	565480
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091341
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Thumb Decoding Accuracy Support vector machines Electrodes Feature extraction
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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