Comparative study of band-power extraction techniques for Motor Imagery classification

Author

Brodu, Nicolas ; Lotte, Fabien ; Lécuyer, Anatole

Author_Institution

INRIA, Rennes, France

fYear

2011

fDate

11-15 April 2011

Firstpage

1

Lastpage

6

Abstract

We review different techniques for extracting the power information contained in frequency bands in the context of electroencephalography (EEG) based Brain-Computer Interfaces (BCI). In this domain it is common to apply only one algorithm for extracting the power information. However previous work and our current study confirm that one may indeed expect varying degrees of success by choosing inadequate algorithms for the power extraction. Our results suggest that on average one algorithm seems superior for extracting the power information for Motor Imagery tasks : the application of a Morlet wavelet on the raw EEG signals, with the time-frequency resolution tradeoff selected by cross-validation.

Keywords

brain-computer interfaces; electroencephalography; feature extraction; image classification; medical image processing; EEG signals; Morlet wavelet; band power extraction technique; electroencephalography based brain computer interfaces; frequency band; motor imagery classification; motor imagery task; power information extraction; time frequency resolution; Accuracy; Brain computer interfaces; Data mining; Estimation; Feature extraction; Spectrogram; Time frequency analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2011 IEEE Symposium on

Conference_Location

Paris

Print_ISBN

978-1-4244-9890-1

Type

conf

DOI

10.1109/CCMB.2011.5952105

Filename

5952105