Generalized features for electrocorticographic BCIs.
This paper studies classifiability of electrocorticographic signals (ECoG) for use in a human brain-computer interface (BCI). The results show that certain spectral features can be reliably used across several subjects to accurately classify different types of movements. Sparse and nonsparse versions of the support vector machine and regularized linear discriminant analysis linear classifiers are assessed and contrasted for the classification problem. In conjunction with a careful choice of features, the classification process automatically and consistently identifies neurophysiological areas known to be involved in the movements. An average two-class classification accuracy of 95% for real movement and around 80% for imagined movement is shown. The high accuracy and generalizability of these results, obtained with as few as 30 data samples per class, support the use of classification methods for ECoG-based BCIs.
Shenoy
Pradeep
P
Department of Computer Science and Engineering, University of Washington, Seattle 98195, USA. pshenoy@cs.washington.edu
Miller
Kai J
KJ
Ojemann
Jeffrey G
JG
Rao
Rajesh P N
RP
D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D014584:User-Computer Interface