Principle component feature detector for motor cortical control

A Comprehensive Survey of Brain Interface Technology Designs

In this work we present the first comprehensive survey of Brain Interface (BI) technology designs published prior to January 2006. Detailed results from this survey, which was based on the Brain Interface Design Framework proposed by Mason and Birch, are presented and discussed to address the following research questions: (1) which BI technologies are directly comparable, (2) what technology designs exist, (3) which application areas (users, activities and environments) have been targeted in these designs, (4) which design approaches have received little or no research and are possible opportunities for new technology, and (5) how well are designs reported. The results of this work demonstrate that meta-analysis of high-level BI design attributes is possible and informative. The survey also produced a valuable, historical cross-reference where BI technology designers can identify what types of technology have been proposed and by whom.

Feature detection in motor cortical spikes by principal component analysis

Principal component analysis was performed on recorded neural spike trains in rats' motor cortices when rats were involved in real-time control tasks using brain-machine interfaces. The rat with implanted microelectrode array was placed in a conditioning chamber, but freely moving, to decide which one of the two paddles should be activated to shift the cue light to the center. It is found that the principal component feature vectors revealed the importance of individual neurons and windows of time in the decision making process. In addition, one of the first principal components has much higher discriminative capability than others, although it represents only a small percentage of the total variance in the data. Using one to six principal components with a Bayes classifier achieved classification accuracy comparable to that obtained by a more sophisticated high performance support vector classifier.