Classification of multichannel ECoG related to individual finger movements with redundant spatial projections
Author
Onaran, ibrahim
İnce, N. Fırat
Çetin, A. Enis
Date
2011Source Title
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher
IEEE
Pages
5424 - 5427
Language
English
Type
Conference PaperItem Usage Stats
170
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128
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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. © 2011 IEEE.
Keywords
Brain machine interfaceClassification accuracy
Common spatial patterns
Electrocorticogram
Finger movements
Frequency ranges
Multi-channel
Multi-class problems
Neural activity
Algorithms
Feature extraction
Frequency bands
Electrophysiology