A method for recognizing high-frequency steady-state visual evoked potential based on empirical modal decomposition and canonical correlation analysis

In most of current studies on SSVEP based BCIs, low-frequency and medium-frequency visual stimuli are used, and subjects are prone to fatigue. The BCI based on high-frequency SSVEP can improve the comfort level of subjects in experiment and reduce the possibility of inducing diseases such as epileps...

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Bibliographic Details
Published in2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) pp. 774 - 778
Main Authors Liu, Wenqian, Zhang, Li, Li, Changsheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2019
Subjects
BCI
Brain-computer interfaces
CCA method
Correlation
Electroencephalography
EMD method
high-frequency SSVEP
Information processing
Steady-state
Time-frequency analysis
Visualization
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DOI10.1109/ITNEC.2019.8729005

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Summary:In most of current studies on SSVEP based BCIs, low-frequency and medium-frequency visual stimuli are used, and subjects are prone to fatigue. The BCI based on high-frequency SSVEP can improve the comfort level of subjects in experiment and reduce the possibility of inducing diseases such as epilepsy. This paper proposed a method combining empirical modal decomposition (EMD) and canonical correlation analysis (CCA) to improve the classification accuracy of high-frequency SSVEP. The experiment results show that the EMD-CCA based method is more suitable for high-frequency SSVEP based BCI, which can achieve a maximum accuracy of 93.68% and an information transmission rate of 15.0236bit/min^{-1}.
DOI:10.1109/ITNEC.2019.8729005