@inproceedings{38efa6119acb4b81bed31fa4ac150ee3,
title = "The sinusoidal assisted MEMD based CCA method for SSVEP based BCI improvement",
abstract = "Although the canonical correlation analysis (CCA) algorithm has been applied successfully to SSVEP detection, artifacts and unrelated brain activities may influence the performance of the steady state visual evoked potential (SSVEP) based brain-computer interfaces (BCI) system. Extracting the characteristic frequency sub-bands is an effective method to enhance the signal-to-noise-ratio of SSVEP signals. The sinusoid-assisted MEMD (SA-MEMD) algorithm is a powerful method for spectral decomposition. In this study, we propose an SA-MEMD based CCA method for SSVEP detection. The results suggest that the SA-MEMD based CCA algorithm is a useful method in the detection of typical SSVEP signals. The classification accuracy achieved 88.3% in a 4 s time window and there was a 2.8% improvement compared with the standard CCA algorithm.",
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 Sheng Ge and Keiji Iramina",
year = "2018",
month = aug,
doi = "10.1109/CICN.2018.8864953",
language = "English",
series = "Proceedings - 2018 10th International Conference on Computational Intelligence and Communication Networks, CICN 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "65--69",
editor = "{Akbar Hussain}, {D. M.} and Tomar, {Geetam Singh} and Tomar, {Geetam Singh}",
booktitle = "Proceedings - 2018 10th International Conference on Computational Intelligence and Communication Networks, CICN 2018",
address = "United States",
note = "10th International Conference on Computational Intelligence and Communication Networks, CICN 2018 ; Conference date: 17-08-2018 Through 19-08-2018",
}