TY - JOUR
T1 - Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain-Computer Interfaces
AU - Ge, Sheng
AU - Shi, Yan Hua
AU - Wang, Rui Min
AU - Lin, Pan
AU - Gao, Jun Feng
AU - Sun, Gao Peng
AU - Iramina, Keiji
AU - Yang, Yuan Kui
AU - Leng, Yue
AU - Wang, Hai Xian
AU - Zheng, Wen Ming
N1 - Funding Information:
Manuscript received September 5, 2017; revised November 1, 2017; accepted November 14, 2017. Date of publication November 20, 2017; date of current version August 31, 2018. This work was supported in part by the National Basic Research Program of China (2015CB351704), in part by the National Nature Science Foundation of China under Grants 61473221, 81271659, 31500881, and 61375118, in part by the Natural Science Foundation of Jiangsu Province of China (BK20140621), and in part by the Fundamental Research Funds for the Central Universities of China. (Corresponding author: Wen-Ming Zheng.) S. Ge, G.-P. Sun, Y.-K. Yang, Y. Leng, H.-X. Wang, and W.-M. Zheng are with the Department of Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China (e-mail: shengge@seu.edu.cn; sungaopeng@seu.edu. cn; yuankui@seu.edu.cn; lengyue@seu.edu.cn; hxwang@seu.edu.cn; wenming_zheng@seu.edu.cn).
Publisher Copyright:
© 2013 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85035748850&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85035748850&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2017.2775657
DO - 10.1109/JBHI.2017.2775657
M3 - Article
C2 - 29990114
AN - SCOPUS:85035748850
SN - 2168-2194
VL - 22
SP - 1373
EP - 1384
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 5
M1 - 8115129
ER -