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A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain-computer

Yi-Feng Chen1, Kiran Atal, Sheng-Quan Xie

  • 1School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, People's Republic of China. Mechanical Engineering, University of Auckland, Auckland, New Zealand.

Journal of Neural Engineering
|March 31, 2017
PubMed
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This study introduces a new method combining multivariate empirical mode decomposition (MEMD) and canonical correlation analysis (CCA) for improved steady-state visual evoked potentials (SSVEP) detection in brain-computer interfaces (BCI). The MEMD-CCA method significantly enhances SSVEP recognition accuracy compared to existing techniques.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Accurate detection of steady-state visual evoked potentials (SSVEP) is crucial for brain-computer interface (BCI) applications.
  • Spontaneous electroencephalogram (EEG) activities and artifacts can degrade the performance of traditional SSVEP recognition methods like canonical correlation analysis (CCA).

Purpose of the Study:

  • To develop and evaluate an improved method for SSVEP detection that mitigates the impact of noise and artifacts.
  • To enhance the accuracy and efficiency of SSVEP recognition for BCI applications.

Main Methods:

  • The study proposes a novel approach, multivariate empirical mode decomposition combined with canonical correlation analysis (MEMD-CCA).
  • EEG signals were recorded from nine healthy volunteers to assess the MEMD-CCA method's performance.

Related Experiment Videos

  • The proposed method was compared against standard CCA and temporally local multivariate synchronization index (TMSI).

    • Main Results:

    • The MEMD-CCA method demonstrated significantly higher accuracy in SSVEP recognition compared to standard CCA and TMSI across various time windows.
    • Specific accuracy improvements over CCA ranged from 1.34% to 18.45%, and over TMSI from 0.55% to 14.67%.
    • The MEMD-CCA approach outperformed CCA methods utilizing filter-based decomposition, empirical mode decomposition, and wavelet decomposition.

    Conclusions:

    • The developed MEMD-CCA method effectively improves the performance of SSVEP-based BCIs.
    • This technique offers a promising solution for more robust and accurate SSVEP detection in noisy EEG environments.