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An Algorithm to Detect P300 Potentials Based on F-Score Channel Selection and Support Vector Machines

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4 Author(s)
Licai Yang ; Shandong Univ., Jinan ; Jinliang Li ; Yucui Yao ; Guanglin Li

To improve the classification accuracy of P300 potentials and the training speed of optimal support vector machines (SVM) classifier, a novel P300 detection algorithm based on F-score channel selection and SVM is proposed in this paper. Using F-score channel selection method, we reduce the task-irrelevant EEG channels to enhance the detection accuracy of P300 potentials. Meanwhile, by a new training set selection method given in this paper, we divide the primal training set into a training set and a validation set. With this validation set, the test error of the SVM classifiers can be predicted more accurately and quickly. Our algorithm was tested with a P300 dataset from the BCI competition 2003. And the results showed that the algorithm achieved an accuracy of 100% in P300 detection within four repetitions.

Published in:

Natural Computation, 2007. ICNC 2007. Third International Conference on  (Volume:2 )

Date of Conference:

24-27 Aug. 2007