@inproceedings{2f2af46f42cf45d185115f699a691a97,
title = "Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement",
abstract = "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%.",
author = "Gaopeng Sun and Yanhua Shi and Hui Liu and Yichuan Jiang and Pan Lin and Junfeng Gao and Ruimin Wang and Yue Leng and Yuankui Yang and Iramina Keiji and Sheng Ge",
year = "2018",
month = jan,
day = "1",
doi = "10.1117/12.2514420",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ruidan Su",
booktitle = "2018 International Conference on Image and Video Processing, and Artificial Intelligence",
address = "United States",
note = "2018 International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2018 ; Conference date: 15-08-2018 Through 17-08-2018",
}