SSVEP Recognition using Wavelet Canonical Correlation Analysis for Brain Computer Interface

DOI

10.2991/icsma-16.2016.87How to use a DOI?

Keywords

Frequency Recognition, Canonical Correlation Analysis (CCA), Electroencephalogram (EEG), Steady-State Visual Evoked Potential (SSVEP), Wavelet Packet Decomposition (WPD), Brain-Computer Interface (BCI)

Abstract

Canonical correlation analysis (CCA)-based methods have been widely applied to frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). However,it is hardly to obtain optimal recognition accuracy from electroencephalogram (EEG) signal mixed with variety of noises. In this paper, we use wavelet packet decomposition to reject components which are not related to stimulus frequency in SSVEP signals to improve SSVEP recognition accuracy. Experimental results show its superior performance over other competing methods.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shaobo Liu
AU  - Fuchun Sun
AU  - Wechang Zhang
AU  - Chuanqi Tan
PY  - 2016/12
DA  - 2016/12
TI  - SSVEP Recognition using Wavelet Canonical Correlation Analysis for Brain Computer Interface
BT  - Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016)
PB  - Atlantis Press
SP  - 497
EP  - 500
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsma-16.2016.87
DO  - 10.2991/icsma-16.2016.87
ID  - Liu2016/12
ER  -