A modified Canonical Correlation Analysis Method for SSVEP Frequency Recognition

Document Type : Power Article

Authors

1 Biomedical Engineering Department, Semnan University, Semnan, Iran

2 semnan university

Abstract

The canonical correlation analysis (CCA) is one of the most widely used frequency recognition methods in steady-state visual evoked potential (SSVEP)-based brain computer interface systems. Although the CCA is often associated with good results, but if stimulation frequencies have harmonic relation, this issue will challenge this method. In this paper, the modified CCA method has been proposed that can solve this problem by adding a post-processing step in the standard CCA. For this purpose, visual stimulus ranged from 6-16 Hz with an interval of 0.5 have been generated using Matlab and the psychophysics toolbox. The SSVEP signal was recorded from ten subjects via one electrode placed at Oz. According to the proposed method, after applying CCA and determining the frequency corresponding to the maximum correlation, the difference between the correlation associated to this frequency and the correlation of the corresponding harmonic frequency is calculated. Then, the frequency is recognized by comparing the obtained value with the threshold. The threshold is determined based on the data of each subject during the offline analysis. For eight-second time window, the average recognition accuracy of the standard CCA with choosing two harmonics in constructing the reference signal (N=2) was 74%, while the corresponding value of the proposed method was 81%. Correspondingly, the accuracy was increased from 78% to 83% for four-second time window. For wide frequency range, the proposed method has been able to improve the frequency recognition accuracy compared with the standard CCA, by reducing harmonic recognition error.

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References

 
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Journal of Modeling in Engineering
Volume 16, Issue 55
December 2018
Pages 199-207

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History

  • Receive Date: 16 May 2017
  • Revise Date: 26 February 2018
  • Accept Date: 23 May 2018

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