Posterior Probability Support Vector Machine Applied in Motor Imagery Classification

Abstract-Brain-computer interface (BCI) which transforms signals from the brain into control signals can help people with disabilities communicate with others. In this paper, posteriori probability support vector machine (PPSVM) for patterns recognition was developed. For the classification of the left or right hand motor imagery, this method was used to expend the training set by adding samples with great probability output. For the dataset from 2003 BCI Competition, AR model was adopted to extract feature vectors and SVM with posteriori probabilistic output was used to classify the dataset. The results proved that, by adding samples with big probability, the performance of BCI was improved and higher accuracy was achieved.