Feature extraction and parameters selection of classification model on brain-computer interface

Brain-computer interface (BCI) is a communication system that connects the brain with the computer and the peripheral equipment. In classification experiment of single-trial electroencephalogram (EEG) for left and right finger movement task, common spatial patterns (CSP) are employed to extract feature for EEG signals, and support vector machines (SVM) are used to classify. Basing on neurophysiological background of EEG signals, a new feature extraction method is proposed to select channel number, position, filter frequency and spatial filter number. Basing on analyzing change feature of the error penalty parameter C and the Gaussian kernel parameter Con support vector machines, a new area search table is proposed to improve classification accuracy.