SSVEP frequency detection methods considering background EEG

Author

Tanaka, T. ; Cheng Zhang ; Higashi, Hiroshi

Author_Institution

Dept. of Electr. & Electron. Eng., Tokyo Univ. of Agric. & Technol., Koganei, Japan

fYear

2012

fDate

20-24 Nov. 2012

Firstpage

1138

Lastpage

1143

Abstract

Detection of the frequency of steady-state visual evoked potentials (SSVEP) is addressed. We propose to use the canonical correlation analysis (CCA) with linear discriminant analysis (LDA), as well as some modifications of the so-called rhythmic component extraction (RCE) that can consider the background EEG spectra Classification accuracy and the information transfer rate (ITR) are examined in classification of six commands.

Keywords

correlation methods; electroencephalography; medical signal processing; signal classification; spectral analysis; CCA; LDA; RCE; SSVEP; background EEG spectra classification accuracy; canonical correlation analysis; command classification; frequency detection method; information transfer rate; linear discriminant analysis; rhythmic component extraction; steady-state visual evoked potential;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on

Conference_Location

Kobe

Print_ISBN

978-1-4673-2742-8

Type

conf

DOI

10.1109/SCIS-ISIS.2012.6505369

Filename

6505369

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2008574