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%.
| Original language | English |
|---|---|
| Title of host publication | 2018 International Conference on Image and Video Processing, and Artificial Intelligence |
| Editors | Ruidan Su |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510623101 |
| DOIs | |
| Publication status | Published - Jan 1 2018 |
| Event | 2018 International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2018 - Shanghai, China Duration: Aug 15 2018 → Aug 17 2018 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 10836 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Conference
| Conference | 2018 International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2018 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 8/15/18 → 8/17/18 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
Sinusoid-assisted MEMD-based CCA method for SSVEP-based BCI improvement. / Sun, Gaopeng; Shi, Yanhua; Liu, Hui; Jiang, Yichuan; Lin, Pan; Gao, Junfeng; Wang, Ruimin; Leng, Yue; Yang, Yuankui; Iramina, Keiji; Ge, Sheng.
2018 International Conference on Image and Video Processing, and Artificial Intelligence. ed. / Ruidan Su. SPIE, 2018. 1083628 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10836).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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 - Iramina, Keiji
AU - Ge, Sheng
PY - 2018/1/1
Y1 - 2018/1/1
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
ER -