The SSVEP topographic scalp maps by Canonical correlation analysis

Author

Bin, Guangyu ; Lin, Zhonglin ; Gao, Xiaorong ; Hong, Bo ; Gao, Shangkai

Author_Institution

Department of Biomedical Engineering, Tsinghua University, Beijing, China

fYear

2008

fDate

20-25 Aug. 2008

Firstpage

3759

Lastpage

3762

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.

Keywords

Brain computer interfaces; Data analysis; Data mining; Electrodes; Electroencephalography; Frequency; Light emitting diodes; Scalp; Signal analysis; Steady-state; Adult; Algorithms; Artificial Intelligence; Cerebral Cortex; Data Interpretation, Statistical; Electroencephalography; Evoked Potentials, Visual; Humans; Models, Statistical; Pattern Recognition, Automated; Statistics as Topic; User-Computer Interface; Visual Cortex;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

Conference_Location

Vancouver, BC

ISSN

1557-170X

Print_ISBN

978-1-4244-1814-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2008.4650026

Filename

4650026