The SSVEP topographic scalp maps by canonical correlation analysis

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

doi:10.1109/IEMBS.2008.4650026

The SSVEP topographic scalp maps by canonical correlation analysis

Annu Int Conf IEEE Eng Med Biol Soc. 2008:2008:3759-62. doi: 10.1109/IEMBS.2008.4650026.

Authors

Guangyu Bin¹, Zhonglin Lin, Xiaorong Gao, Bo Hong, Shangkai Gao

Affiliation

¹ Department of Biomedical Engineering, Tsinghua University, Beijing, China.

PMID: 19163529
DOI: 10.1109/IEMBS.2008.4650026

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Algorithms
Artificial Intelligence
Cerebral Cortex / physiology*
Data Interpretation, Statistical
Electroencephalography / methods*
Evoked Potentials, Visual / physiology*
Humans
Models, Statistical
Pattern Recognition, Automated / methods*
Statistics as Topic
User-Computer Interface
Visual Cortex / physiology*