DocumentCode :
2722024
Title :
Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals
Author :
Nai-Jen, Huan ; Palaniappan, Ramaswamy
Author_Institution :
Fac. of Inf. Sci. & Technol., Multimedia Univ., Melaka, Malaysia
Volume :
1
fYear :
2004
fDate :
1-5 Sept. 2004
Firstpage :
507
Lastpage :
510
Abstract :
Classification of EEG signals extracted during mental tasks is a technique for designing brain computer interfaces (BCI). We classify EEG signals that were extracted during mental tasks using fixed autoregressive (FAR) and adaptive AR (AAR) models. Five different mental tasks from 4 subjects were used in the experimental study and combinations of 2 different mental tasks are studied for each subject. Four different feature extraction methods were used to extract features from these EEG signals: FAR coefficients computed with Burg´s algorithm using 125 data points, without segmentation and with segmentation of 25 data points, AAR coefficients computed with least-mean-square (LMS) algorithm using 125 data points, without segmentation and with segmentation of 25 data points. Multilayer perception (MLP) neural network (NN) trained by the backpropagation (BP) algorithm is used to classify these features into the different categories representing the mental tasks. The best results for FAR was 92.70% while for AAR was only 81.80%. The results obtained here indicated that FAR using 125 data points without segmentation gave better classification performance as compared to AAR, with all other parameters constant.
Keywords :
autoregressive processes; backpropagation; electroencephalography; feature extraction; least mean squares methods; medical signal processing; multilayer perceptrons; physiological models; signal classification; Burg algorithm; EEG signals; adaptive autoregressive model; backpropagation algorithm; brain computer interfaces; feature extraction; fixed autoregressive model; least-mean-square algorithm; mental task classification; multilayer perception neural network; signal segmentation; Backpropagation algorithms; Brain computer interfaces; Brain modeling; Data mining; Electroencephalography; Feature extraction; Least squares approximation; Multi-layer neural network; Neural networks; Signal design;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-8439-3
Type :
conf
DOI :
10.1109/IEMBS.2004.1403205
Filename :
1403205
Link To Document :
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