caa a22 4500 606159800 CHVBK 20210128100626.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00521-014-1753-3 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1753-3 Discriminating the brain activities for brain-computer interface applications through the optimal allocation-based approach [Elektronische Daten] [Siuly Siuly, Yan Li] 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. The Natural Computing Applications Forum, 2014 Brain-computer interface (BCI) nationallicence Electroencephalogram (EEG) nationallicence Motor imagery (MI) nationallicence Optimal allocation (OA) nationallicence Least square support vector machine (LS-SVM) nationallicence Naive Bayes (NB) nationallicence Siuly Siuly Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC, Australia aut Li Yan Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD, Australia aut Neural Computing and Applications Springer London 26/4(2015-05-01), 799-811 0941-0643 26:4<799 2015 26 521 https://doi.org/10.1007/s00521-014-1753-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1753-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Siuly Siuly Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC, Australia aut NATIONALLICENCE 700 1- Li Yan Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD, Australia aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/4(2015-05-01), 799-811 0941-0643 26:4<799 2015 26 521