Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 576437, 12 pages
Research Article

Exploring Sampling in the Detection of Multicategory EEG Signals

1Centre for Applied Informatics, College of Engineering and Science, Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia
2School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia
3School of Computer Science, Fudan University, Shanghai 200433, China

Received 24 February 2015; Accepted 30 March 2015

Academic Editor: Po-Hsiang Tsui

Copyright © 2015 Siuly Siuly et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.