SSVEP-based BCI classification using power cepstrum analysis

The power cepstrum-based parameters for steady-state visually evoked potential (SSVEP) is proposed. To precisely represent the characteristics of frequency responses of a visually stimulated electroencephalography (EEG) signal, power cepstrum analysis is adopted to estimate the parameters in low-dimensional space. To represent the frequency responses of SSVEP, the log-magnitude spectrum of an EEG signal is estimated by fast Fourier transform. Subsequently, the discrete cosine transform is applied to linearly transform the log-magnitude spectrum into the cepstrum domain, and then generate a set of coefficients. Finally, a Bayesian decision model with a Gaussian mixture model is adopted to classify the responses of SSVEP. The experimental results demonstrated that the proposed approach was able to improve performance compared with previous approaches and was suitable for use in brain computer interface applications.