TY - GEN
T1 - Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement
AU - Sun, Gaopeng
AU - Shi, Yanhua
AU - Liu, Hui
AU - Jiang, Yichuan
AU - Lin, Pan
AU - Gao, Junfeng
AU - Wang, Ruimin
AU - Leng, Yue
AU - Yang, Yuankui
AU - Keiji, Iramina
AU - Ge, Sheng
N1 - Funding Information:
This work was supported by the National Basic Research Program of China (2015CB351704), National Nature Science Foundation of China (61473221, 31500881, 61773408, 81271659), and Fundamental Research Funds for the Southeast University.
Publisher Copyright:
© 2018 SPIE.
PY - 2018
Y1 - 2018
N2 - Although the canonical correlation analysis (CCA) algorithm has been applied successfully to steady-state visual evoked potential (SSVEP) detection, artifacts and unrelated brain activities may affect the performance of SSVEP-based brain-computer interface systems. Extracting the characteristic frequency sub-bands is an effective method of enhancing the signal-to-noise-ratio of SSVEP signals. The sinusoid-assisted multivariate extension of empirical mode decomposition (SA-MEMD) algorithm is a powerful method of spectral decomposition. In this study, we propose an SA-MEMD-based CCA method for SSVEP detection. Experimental results suggest that the SA-MEMD-based CCA algorithm is a useful method for the detection of typical SSVEP signals. The SA-MEMD-based CCA algorithm reached a classification accuracy of 88.3% for a window of 4 s and outperformed the standard CCA algorithm by 2.8%.
AB - Although the canonical correlation analysis (CCA) algorithm has been applied successfully to steady-state visual evoked potential (SSVEP) detection, artifacts and unrelated brain activities may affect the performance of SSVEP-based brain-computer interface systems. Extracting the characteristic frequency sub-bands is an effective method of enhancing the signal-to-noise-ratio of SSVEP signals. The sinusoid-assisted multivariate extension of empirical mode decomposition (SA-MEMD) algorithm is a powerful method of spectral decomposition. In this study, we propose an SA-MEMD-based CCA method for SSVEP detection. Experimental results suggest that the SA-MEMD-based CCA algorithm is a useful method for the detection of typical SSVEP signals. The SA-MEMD-based CCA algorithm reached a classification accuracy of 88.3% for a window of 4 s and outperformed the standard CCA algorithm by 2.8%.
UR - http://www.scopus.com/inward/record.url?scp=85062475540&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062475540&partnerID=8YFLogxK
U2 - 10.1117/12.2514420
DO - 10.1117/12.2514420
M3 - Conference contribution
AN - SCOPUS:85062475540
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - 2018 International Conference on Image and Video Processing, and Artificial Intelligence
A2 - Su, Ruidan
PB - SPIE
T2 - 2018 International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2018
Y2 - 15 August 2018 through 17 August 2018
ER -