Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain-Computer Interfaces

Sheng Ge, Yan Hua Shi, Rui Min Wang, Pan Lin, Jun Feng Gao, Gao Peng Sun, Keiji Iramina, Yuan Kui Yang, Yue Leng, Hai Xian Wang, Wen Ming Zheng

研究成果: ジャーナルへの寄稿学術誌査読

16 被引用数 (Scopus)


A brain-computer interface (BCI) is a communication approach that permits cerebral activity to control computers or external devices. Brain electrical activity recorded with electroencephalography (EEG) is most commonly used for BCI. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is a data-driven time-frequency analysis method that can be applied to nonlinear and nonstationary EEG signals for BCI data processing. However, because white Gaussian noise occupies a broad range of frequencies, some redundant components are introduced. To solve this leakage problem, in this study, we propose using a sinusoidal assisted signal that occupies the same frequency ranges as the original signals to improve MEMD performance. To verify the effectiveness of the proposed sinusoidal signal assisted MEMD (SA-MEMD) method, we compared the decomposition performances of MEMD, NA-MEMD, and the proposed SA-MEMD using synthetic signals and a real-world BCI dataset. The spectral decomposition results indicate that the proposed SA-MEMD can avoid the generation of redundant components and over decomposition, thus, substantially reduce the mode mixing and misalignment that occurs in MEMD and NA-MEMD. Moreover, using SA-MEMD as a signal preprocessing method instead of MEMD or NA-MEMD can significantly improve BCI classification accuracy and reduce calculation time, which indicates that SA-MEMD is a powerful spectral decomposition method for BCI.

ジャーナルIEEE Journal of Biomedical and Health Informatics
出版ステータス出版済み - 9月 2018

!!!All Science Journal Classification (ASJC) codes

  • バイオテクノロジー
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 健康情報管理


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