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

Nai-Jen, H., Palaniappan, Ramaswamy (2004) Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals. In: Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 26 I. pp. 507-510. IEEE ISBN 0-7803-8439-3. (doi:10.1109/IEMBS.2004.1403205) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/IEMBS.2004.1403205

Abstract

Classification of EEG signals extracted during mental tasks is a technique for designing Brain Computer Interfaces (BCI). In this paper, 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 Perceptron (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.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/IEMBS.2004.1403205
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Annu Int Conf IEEE Eng Med Biol Proc [Field not mapped to EPrints] AD - Fac. of Info. Science and Technology, Multimedia University, 75450, Melaka, Malaysia [Field not mapped to EPrints] AD - Biomed. Engineering Research Centre, Nanyang Technological University, Singapore, Singapore [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] A4 - Institute of Electrical and Electronics Engineers, IEEE; IEEE Engineering in Medicine and Biology Society [Field not mapped to EPrints] C3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings [Field not mapped to EPrints]
Uncontrolled keywords: Algorithms, Bioelectric potentials, Brain, Interfaces (computer), Multilayer neural networks, Psychophysiology, Regression analysis, Synchronization, Adaptive models, Fixed autoregressive models, Mental tasks, Electroencephalography
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 12:14 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70752 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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