TY - GEN
T1 - Detection of steady-state visual-evoked potential using differential canonical correlation analysis
AU - Wei, Chun-Shu
AU - Lin, Yuan Pin
AU - Wang, Yijun
AU - Wang, Yu Te
AU - Jung, Tzyy Ping
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Steady-state visual evoked potential (SSVEP) is an electroencephalogram (EEG) activity elicited by periodic visual flickers. Frequency-coded SSVEP has been commonly adopted for functioning brain-computer interfaces (BCIs). Up to date, canonical correlation analysis (CCA), a multivariate statistical method, is considered to be state-of-the-art to robustly detect SSVEPs. However, the spectra of EEG signals often have a 1/f power-law distribution across frequencies, which inherently confines the CCA efficiency in discriminating between high-frequency SSVEPs and low-frequency background EEG activities. This study proposes a new SSVEP detection method, differential canonical correlation analysis (dCCA), by incorporating CCA with a notch-filtering procedure, to alleviate the frequency-dependent bias. The proposed dCCA approach significantly outperformed the standard CCA approach by around 6% in classifying SSVEPs at five frequencies (9-13Hz). This study could promote the development of high-performance SSVEP-based BCI systems.
AB - Steady-state visual evoked potential (SSVEP) is an electroencephalogram (EEG) activity elicited by periodic visual flickers. Frequency-coded SSVEP has been commonly adopted for functioning brain-computer interfaces (BCIs). Up to date, canonical correlation analysis (CCA), a multivariate statistical method, is considered to be state-of-the-art to robustly detect SSVEPs. However, the spectra of EEG signals often have a 1/f power-law distribution across frequencies, which inherently confines the CCA efficiency in discriminating between high-frequency SSVEPs and low-frequency background EEG activities. This study proposes a new SSVEP detection method, differential canonical correlation analysis (dCCA), by incorporating CCA with a notch-filtering procedure, to alleviate the frequency-dependent bias. The proposed dCCA approach significantly outperformed the standard CCA approach by around 6% in classifying SSVEPs at five frequencies (9-13Hz). This study could promote the development of high-performance SSVEP-based BCI systems.
UR - http://www.scopus.com/inward/record.url?scp=84897723083&partnerID=8YFLogxK
U2 - 10.1109/NER.2013.6695870
DO - 10.1109/NER.2013.6695870
M3 - Conference contribution
AN - SCOPUS:84897723083
SN - 9781467319690
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 57
EP - 60
BT - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
T2 - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Y2 - 6 November 2013 through 8 November 2013
ER -