Open AccessSSVEP‐based BCI classification using power cepstrum analysis
Author(s) -
Chen YeouJiunn,
See Aaron Raymond Ang,
Chen ShihChung
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.0173
Subject(s) - brain–computer interface , cepstrum , computer science , speech recognition , pattern recognition (psychology) , artificial intelligence , power (physics) , electroencephalography , psychology , neuroscience , physics , quantum mechanics
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.