EEG-based discrimination of elbow/shoulder torques using brain computer interface algorithms: Implications for rehabilitation

Brain computer interface (BCI) algorithms are used to predict the torque generation in the direction of shoulder abduction or elbow flexion using scalp EEG signals from 163 electrodes. Based on features extracted from both frequency and time domains, three classifiers are employed including support vector classifier, classification trees and K nearest neighbor. Support vector classifier achieves the highest recognition rate of 92.9% on two able-bodied subjects in average. The recognition rates we obtained on the able-bodied subjects are among the highest compared with previous reports on predicting motor intent using scalp EEG. This demonstrates the feasibility of separating the shoulder/elbow torques using scalp EEG as well as the potential of support vector classifier in applications of BCI. Preliminary experiments on two hemiparetic stroke subjects using support vector classifier reports an accuracy of 84.1% in average, which shows an increased difficulty in predicting intent presumably due to cortical reorganization resulting from the stroke.