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Improving the performance of two-state mental task brain-computer interface design using linear discriminant classifier

Palaniappan, Ramaswamy, Huan, N.-J. (2005) Improving the performance of two-state mental task brain-computer interface design using linear discriminant classifier. In: EUROCON 2005. I. pp. 409-412. ISBN 978-1-4244-0049-2. (doi:10.1109/EURCON.2005.1629949) (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/EURCON.2005.1629949

Abstract

The purpose of this study is to motivate the use of the simpler Linear Discriminant (LD) classifier as compared to the commonly used Multilayer-perceptron-backpropagation (MLP-BP) neural network for Brain Computer Interface (BCI) design. We investigated the performances of MLP-BP and LD classifiers for mental task based BCI design. In the experimental study, EEG signals from five mental tasks were recorded from four subjects and the classification performances of different combinations of two mental tasks were studied for each subject. Two different AR models were used to compute the features from the electroencephalogram signals: Burg's algorithm (ARB) and Least Square algorithm (ARLS). The results showed that in most cases, LD classifier gave superior classification performance as compared to MLP-BP, with reduced computational complexity. However, the best mental tasks for each subject were the same using both classifiers. ARLS gave the best performance (93.10%) using MLP-BP and (97.00%) using LD. As the best mental task combinations varied between subjects, we conclude that for different subjects, proper selection of mental tasks and feature extraction methods would be essential for a BCI design.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/EURCON.2005.1629949
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - EUROCON 2005 Int. Conf. on Comp. Tool [Field not mapped to EPrints] AD - IEEE, United Kingdom [Field not mapped to EPrints] AD - Dept. of Computer Science, University of Essex, Colchester, CO4 3SQ, United Kingdom [Field not mapped to EPrints] AD - Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] C3 - EUROCON 2005 - The International Conference on Computer as a Tool [Field not mapped to EPrints]
Uncontrolled keywords: Autoregressive, Electroencephalogram, Neural network, Autoregressive, Brain Computer Interface (BCI) design, Linear discriminant classifiers, Mental tasks, Algorithms, Backpropagation, Computer aided design, Electroencephalography, Least squares approximations, Neural networks, Interfaces (computer)
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 17:19 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70740 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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