title: The SSVEP topographic scalp maps by canonical correlation analysis.
abstract: As the number of electrodes increases, topographic scalp mapping methods for electroencephalogram (EEG) data analysis are becoming important. Canonical correlation analysis (CCA) is a method of extracting similarity between two data sets. This paper presents an EEG topographic scalp mapping -based CCA for the steady-state visual evoked potentials (SSVEP) analysis. Multi-channel EEG data and the sinusoidal reference signal were used as the inputs of CCA. The output linear combination was then employed for mapping. Our experimental results prove the topographic scalp mapping-based CCA can instruct for the improvement of SSVEP-based brain computer interface (BCI) system.
authors.affiliation: Department of Biomedical Engineering, Tsinghua University, Beijing, China.
authors.forename: Guangyu
authors.initials: G
authors.lastname: Bin
authors.forename: Zhonglin
authors.initials: Z
authors.lastname: Lin
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Gao
authors.forename: Bo
authors.initials: B
authors.lastname: Hong
authors.forename: Shangkai
authors.initials: S
authors.lastname: Gao
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D002540:Cerebral Cortex / Q000502:physiology*; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D013223:Statistics as Topic; D014584:User-Computer Interface; D014793:Visual Cortex / Q000502:physiology*