Classification of movement-related single-trial MEG data using adaptive spatial filter

Author

Asano, Futoshi ; Kimura, Masahiro ; Sekiguchi, Tatsuhiko ; Kamitani, Yukiyasu

Author_Institution

Honda Res. Inst. Japan

fYear

2009

fDate

19-24 April 2009

Firstpage

357

Lastpage

360

Abstract

In this paper, a method for extracting and classifying movement-related brain signals is proposed. A single-trial MEG observation is first processed with a pre-whitening filter so that strong stationary interference is eliminated. Next, a brain signal effective for classification is extracted using an adaptive spatial filter. The extracted signal is then classified with a support vector machine. From the experimental results, it is shown that the classification rate of 62.6 % is obtained for the brain signals related to the three types of hand movements (ldquoscissors-paper-rockrdquo).

Keywords

adaptive filters; interference suppression; magnetoencephalography; medical signal processing; signal classification; spatial filters; support vector machines; adaptive spatial filter; movement-related brain signal; prewhitening filter; signal classification; signal extraction; single-trial MEG observation; stationary interference elimination; support vector machine; Adaptive filters; Data mining; Feature extraction; Interference elimination; Principal component analysis; Rhythm; Spatial filters; Support vector machine classification; Support vector machines; Training data; GEVD; MEG; SVM; adaptive spatial filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on

Conference_Location

Taipei

ISSN

1520-6149

Print_ISBN

978-1-4244-2353-8

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2009.4959594

Filename

4959594