Classification of Single-Trial EEG Based on Support Vector Clustering during Finger Movement

Authors:
Boyu Wang;Feng Wan
Affiliations:
Department of Electrical and Electronics Engineering, Faculty of Science and Technology, University of Macau,;Department of Electrical and Electronics Engineering, Faculty of Science and Technology, University of Macau,
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

Citing 3
Cited 1

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Introduction to statistical pattern recognition (2nd ed.)
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Abstract

Classification of electroencephalogram (EEG) is an important and challenging issue for brain computer interface (BCI) system. In this paper, an algorithm based on common spatial subspace decomposition (CSSD) and support vector clustering (SVC) is proposed to classify single-trial EEG recording during left or right finger movement. The algorithm is tested by the dataset IV of "BCI competition 2003", and the experimental result shows the proposed method, only using bereitschaftspotential (BP), rather than both BP and event-related desynchronization (ERD), has higher classification accuracy than the best one reported in the competition.