Discriminating the brain activities for brain–computer interface applications through the optimal allocation-based approach

Siuly, Siuly and Li, Yan (2015) Discriminating the brain activities for brain–computer interface applications through the optimal allocation-based approach. Neural Computing and Applications, 26 (4). pp. 799-811. ISSN 0941-0643

Abstract

The translation of brain activities into signals in
brain–computer interface (BCI) systems requires a robust
and accurate classification to develop a communication
system for motor disabled people. In BCIs, motor imagery
(MI) tasks generate brain activities, which are generally
measured by electroencephalogram (EEG) signals. The aim
of this research was to introduce a method for the extraction of discriminatory information from the MI-based EEG signals for BCI applications. The proposed scheme develops an optimal allocation (OA)-based approach to discover the most effective representatives with minimal variability from a large number of MI-based EEG data. To investigate a suitable classifier for the OA-based features, the least square support vector machine (LS-SVM) and Naive Bayes (NB) methods are applied separately on the extracted features for discriminating the MI activities. Experimental results on datasets, IVa and IVb of BCI Competition III, show that the OA-based features with the LS-SVM classifier yields better performances compared to the NB classifiers. The results also demonstrate that the classification performance is improved up to 21.16%, through the use of the OA algorithm with the LS-SVM, compared to the reported four prominent methods. This implies that the OA technique is promising for extracting representative characteristics from MI-based EEG data, which can be reliably used with the LS-SVM to identify different signals of brain activities in the development of BCI systems.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright The Natural Computing Applications Forum 2014. Permanent restricted access to Published version due to publisher copyright policy.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 13 Apr 2015 23:35
Last Modified: 04 Apr 2017 22:55
Uncontrolled Keywords: brain–computer interface (BCI); electroencephalogram (EEG); motor imagery (MI); optimal allocation (OA); least square support vector machine (LS-SVM); Naive Bayes (NB)
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
17 Psychology and Cognitive Sciences > 1702 Cognitive Sciences > 170201 Computer Perception, Memory and Attention
17 Psychology and Cognitive Sciences > 1702 Cognitive Sciences > 170203 Knowledge Representation and Machine Learning
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology
E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1007/s00521-014-1753-3
URI: http://eprints.usq.edu.au/id/eprint/26307

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