Title :
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
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;
Conference_Titel :
Signal Acquisition and Processing, 2009. ICSAP 2009. International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-0-7695-3594-4
DOI :
10.1109/ICSAP.2009.19
