Motor imaginary signal classification using adaptive recursive bandpass filter and adaptive autoregressive models for brain machine interface designs

Jeyabalan, V. and Samraj, A. and Kiong, L.C. (2008) Motor imaginary signal classification using adaptive recursive bandpass filter and adaptive autoregressive models for brain machine interface designs. International Journal of Biological and Medical Sciences, 3 (4). pp. 231-238.

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Abstract

The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.

Item Type: Article
Uncontrolled Keywords: Adaptive autoregressive, Adaptive bandpass filter,Brain Machine Interface, EEG , Motor imaginary
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Computer Science & Information Technology > Dept of Artificial Intelligence
Depositing User: Miss Nur Jannatul Adnin Ahmad Shafawi
Date Deposited: 19 Mar 2013 00:18
Last Modified: 19 Mar 2013 00:18
URI: http://eprints.um.edu.my/id/eprint/5162

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