Probabilistic Methods in Multi-Class Brain-Computer Interface |
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| Authors: | Ping Yang Xu Lei Tie-Jun Liu Peng Xu De-Zhong Yao |
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| Affiliation: | Key Laboratory for NeuroInformation of Ministry of Education,School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu,610054,China |
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| Abstract: | 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. |
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| Keywords: | Bayesian linear discriminant analysis brain-computer interface kappa coefficient support vector machine |
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