Canonical correlation analysis neural network for steady-state visual evoked potentials based brain-computer interfaces

Authors:
Ka Fai Lao;Chi Man Wong;Feng Wan;Pui In Mak;Peng Un Mak;Mang I Vai
Affiliations:
Department of Electrical and Computer Engineering Faculty of Science and Technology, University of Macau, Macau, China;Department of Electrical and Computer Engineering Faculty of Science and Technology, University of Macau, Macau, China;Department of Electrical and Computer Engineering Faculty of Science and Technology, University of Macau, Macau, China;Department of Electrical and Computer Engineering Faculty of Science and Technology, University of Macau, Macau, China;Department of Electrical and Computer Engineering Faculty of Science and Technology, University of Macau, Macau, China;Department of Electrical and Computer Engineering Faculty of Science and Technology, University of Macau, Macau, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

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Abstract

Canonical correlation analysis (CCA) is a promising feature extraction technique of steady state visual evoked potential (SSVEP)-based brain computer interface (BCI). Many researches have showed that CCA performs significantly better than the traditional methods. In this paper, the neural network implementation of CCA is used for the frequency detection and classification in SSVEP-based BCI. Results showed that the neural network implementation of CCA can achieve higher classification accuracy than the method of power spectral density analysis (PSDA), minimum energy combination (MEC) and similar performance to the standard CCA method.