Classification-guided feature selection for NIRS-based BCI

Motor movements induce distinct patterns in the hemodynamics of the motor cortex, which may be captured by Near-Infrared Spectroscopy (NIRS) for Brain Computer Interfaces (BCI). We present a classification-guided (wrapper) method for time-domain NIRS feature extraction to classify left and right hand movements. Four different wrapper methods, based on univariate and multivariate ranking and sequential forward and backward selection, along with three different classifiers (k-Nearest neighbor, Bayes, and Support Vector Machines) were studied. Using NIRS data from two subjects we show that a rank-based wrapper in conjunction with polynomial SVMs can achieve 100% sensitivity and specificity separating left and right hand movements (5-fold cross validation). Results show the promise of wrapper methods in classifying NIRS signals for BCI applications.