Classifying EEG for Brain Computer Interfaces Using Gaussian Process
Résumé
Classifying electroencephalography (EEG) signals is an important step for proceeding EEG-based brain computer interfaces (BCI). Currently, kernel based methods such as sup- port vector machine (SVM) are the state-of-the-art methods for this problem. In this paper, we apply Gaussian process (GP) classfi¯cation to binary classi¯cation problems of motor imaging EEG data. Comparing with SVM, GP based method naturally provides a predic- tive probability for identifying a trusted prediction which can be used for post-processing in a BCI. Experimental results show that the classification methods based on Gaussian process outperform SVM and K-Nearest Neighbor (KNN) in terms of 0-1 loss class prediction error.
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