Siuly Siuly, Kabir Enamul, Wang Hua, Zhang Yanchun
Centre for Applied Informatics, College of Engineering and Science, Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia.
School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
Comput Math Methods Med. 2015;2015:576437. doi: 10.1155/2015/576437. Epub 2015 Apr 21.
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.
本文提出了一种基于采样和机器学习技术的结构，用于检测多类别脑电信号，其中探讨了随机采样（RS）和最优分配采样（OS）。在所提出的框架中，在使用RS和OS方案之前，根据特定时间段将每个类别的整个脑电信号划分为几个组。使用RS和OS方案以便从脑电数据每个类别的每组中获得代表性观测值。然后，由RS从每个类别的组中选择的所有样本被组合成一个名为RS集的集合。以类似的方式，对于OS方案，获得一个OS集。然后分别从RS集和OS集中提取11个统计特征。最后，本研究采用三种著名的分类器：k近邻（k-NN）、带岭估计器的多项逻辑回归（MLR）和支持向量机（SVM）来评估RS和OS特征集的性能。实验结果表明，RS方案能很好地代表脑电信号，并且使用RS的k-NN是检测多类别脑电信号的最佳选择。
