Frequency Recognition Based on Wavelet-Independent Component Analysis for SSVEP-Based BCIs

  • Limin Yang
  • Ze Wang
  • Chi Man Wong
  • Feng Wan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)


Among the EEG-based BCIs, SSVEP-based BCIs have gained much attention due to the advantages of relatively high information transfer rate (ITR) and short calibration time. Although in SSVEP-based BCIs the frequency recognition methods using multiple channels EEG signals may provide better accuracy, using single channel would be preferable in a practical scenario since it can make the system simple and easy-to-use. To this goal, we propose a new single channel method based on wavelet-independent component analysis (WICA) in the SSVEP-based BCI, in which wavelet transform (WT) is applied to decompose a single channel signal into several wavelet components and then independent component analysis (ICA) is applied to separate the independent sources from the wavelet components. Experimental results show that most of the time the recognition accuracy of the proposed single channel method is higher than the conventional single channel method, power spectrum (PS) method.


wavelet-independent component analysis (WICA) SSVEP frequency recognition BCI 


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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Limin Yang
    • 1
  • Ze Wang
    • 1
  • Chi Man Wong
    • 1
  • Feng Wan
    • 1
  1. 1.Department of Electrical and Computer Engineering, Faculty of Science and TechnologyUniversity of MacauMacauChina

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