Exploring sampling in the detection of multicategory EEG signals

Siuly, Siuly and Kabir, Enamul and Wang, Hua and Zhang, Yanchun (2015) Exploring sampling in the detection of multicategory EEG signals. Computational and Mathematical Methods in Medicine, 2015. ISSN 1748-670X

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The paper presents a structure based on samplings and machine leaning techniques for the detection of multicategory EEG signals where random sampling (RS) and optimal allocation sampling (OS) are explored. In the proposed framework, before using the RS and OS scheme, the entire EEG signals of each class are partitioned into several groups based on a particular time period.The
RS and OS schemes are used in order to have representative observations from each group of each category of EEG data.Then all of the selected samples by the RS from the groups of each category are combined in a one set named RS set. In the similar way, for the OS scheme, an OS set is obtained.Then eleven statistical features are extracted from the RS and OS set, separately. Finally this study employs three well-known classifiers: k-nearest neighbor (k-NN), multinomial logistic regression with a ridge estimator (MLR), and support vector machine (SVM) to evaluate the performance for the RS and OS feature set. The experimental outcomes demonstrate that the RS scheme well represents the EEG signals and the k-NN with the RS is the optimum choice for detection of multicategory EEG signals.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright © 2015 Siuly Siuly et al. This is an open access article distributed under the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 16 Mar 2016 01:54
Last Modified: 13 Feb 2017 02:44
Uncontrolled Keywords: sampling, random sampling (RS), optimal allocation sampling (OS)
Fields of Research : 09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
Identification Number or DOI: 10.1155/2015/576437
URI: http://eprints.usq.edu.au/id/eprint/27519

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