Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals

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 us...

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Vydáno v:Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Ročník 1; s. 507 - 510
Hlavní autoři: Huan Nai-Jen, Palaniappan, R.
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 2004
Témata:
Backpropagation algorithms
Brain computer interfaces
Brain modeling
Data mining
Electroencephalography
Feature extraction
Least squares approximation
Multi-layer neural network
Neural networks
Signal design
ISBN:9780780384392, 0780384393
ISSN:1557-170X
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Shrnutí: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.
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ISBN:9780780384392
0780384393
ISSN:1557-170X
DOI:10.1109/IEMBS.2004.1403205