Method for EEG Feature Extraction Based on Morphological Pattern Spectrum

Author

Han, L.J. ; Zhang, L.J. ; Yang, J.H. ; Li, M. ; Xu, J.W.

Author_Institution

Mech. Eng. Sch., Univ. of Sci. & Technol. Beijing, Beijing, China

fYear

2009

fDate

3-5 April 2009

Firstpage

68

Lastpage

72

Abstract

In order to classify the mental tasks in brain-computer interfaces(BCI), a feature extraction method based on morphological pattern spectrum is here proposed. Flat morphological structure element is selected according to the characteristics of electroencephalography(EEG) and morphological features of different scales are obtained with pattern spectrum. Then, support vector machines(SVM) is used as the classifier. Testing results show that the average classification accuracy is up to 97.7% for two kinds of mental tasks and 93.0% for five kinds of mental tasks. This method has a simple calculation and effective feature extraction performance, so it could be a valid method for real time control of EEG.

Keywords

brain-computer interfaces; electroencephalography; feature extraction; medical signal processing; signal classification; spectral analysis; support vector machines; BCI system; EEG classification; EEG feature extraction; SVM classifier; brain-computer interfaces; electroencephalography; mental tasks; morphological pattern spectrum; support vector machines; Brain; Electroencephalography; Feature extraction; Filters; Morphology; Probes; Shape; Signal processing; Support vector machine classification; Support vector machines; feature extraction; mental task; pattern spectrum; support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Acquisition and Processing, 2009. ICSAP 2009. International Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-0-7695-3594-4

Type

conf

DOI

10.1109/ICSAP.2009.19

Filename

5163827