A classification algorithm of a SSVEP brain-computer interface based on CCA fusion FFT
In order to solve the problems of low recognition accuracy and low information transmission rate of SSVEP EEG signals with multi-target stimulation paradigm,a method combining fast Fourier transform and typical correlation analysis was proposed using fast Fourier transform to train the signal into a training model corresponding to the frequency,and which was used as a reference signal to perform typical correlation analysis with the real-time acquired signal to calculate the recognition accuracy of the frequency.Six subjects participated and completed 180 sets of experiments.Under the condition of time window length of 1.5 s,the average recognition accuracy of the FFT-CCA-based SSVEP signal recognition algorithm was 93.98%,which was 14.75%better than the CCA algorithm,and the information transmission rate was 62.30 bit·min-1,which was 55.63%better than the CCA algorithm.The experimental results showed that the FFT-CCA algorithm has better performance and has good application prospects.
NETL
NSTL
万方数据
