Probabilistic Methods in Multi-Class Brain-Computer Interface

摘要

Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface（BCI）:support vector machine（SVM） with posteriori probability（PSVM） and Bayesian linear dis-criminant analysis with probabilistic output（PBLDA）.A comparative evaluation of these two methods is conducted.The results shows that:1) probabilistic information can improve the performance of BCI for subjects with high kappa coefficient,and 2) PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.