EEG Feature extraction using parcor coefficients

This study presents pattern recognition experiments of the electroencephalogram. The components of the feature vector are built up by Parcor coefficients which provide a simple structure of the covariance matrix. The BAYES classifier is implemented which is theoretically best in minimizing the error rate. The MAHALANOBIS classifier is used too by means of an averaged covariance matrix. The performance of the classifier is tested in experiment by computing the error rate.