title: Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis.
abstract: In this study, a novel method of phase constrained canonical correlation analysis (p-CCA) is presented for classifying steady-state visual evoked potentials (SSVEPs) using multichannel electroencephalography (EEG) signals. p-CCA is employed to improve the performance of the SSVEP-based brain-computer interface (BCI) system using standard CCA. SSVEP response phases are estimated based on the physiologically meaningful apparent latency and are added as a reliable constraint into standard CCA. The results of EEG experiments involving 10 subjects demonstrate that p-CCA consistently outperforms standard CCA in classification accuracy. The improvement is up to 6.8% using 1-4 s data segments. The results indicate that the reliable measurement of phase information is of importance in SSVEP-based BCIs.
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China.
authors.forename: Jie
authors.initials: J
authors.lastname: Pan
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Gao
authors.forename: Fang
authors.initials: F
authors.lastname: Duan
authors.forename: Zheng
authors.initials: Z
authors.lastname: Yan
authors.forename: Shangkai
authors.initials: S
authors.lastname: Gao
mesh_terms: D000328:Adult; D000465:Algorithms; D003627:Data Interpretation, Statistical; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D014584:User-Computer Interface; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*; D055815:Young Adult