Multiple classifier system for EEG signal classification with application to brain-computer interfaces
Multiple classifier system for EEG signal classification with application to brain-computer interfaces
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DOI：
10.1007/s00521-012-1074-3
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发表时间：
2013-10-01
影响因子：
6
通讯作者：
Bagheri, Nasoor
中科院分区：
文献类型：
--
作者：
Ahangi, Amir;Karamnejad, Mehdi;Bagheri, Nasoor
在本文中，我们演示了使用多分类器系统分类的脑电图（EEG）信号。本文的主要目的是应用几种方法来分类运动图像起源于大脑在一个更强大的方式。在这项研究中，使用了BCI竞赛III的数据集II。为了从脑信号中提取特征，使用离散小波变换分解。然后，几个经典的分类器被实现用于在多分类器系统，它优于其他方法的报告结果的数据集上。此外，各种分类器组合方法沿着遗传算法的特征选择进行了评估和比较，以减少分类错误。我们的研究结果表明，集成系统可以提高EEG分类的准确性。
In this paper, we demonstrate the use of a multiple classifier system for classification of electroencephalogram (EEG) signals. The main purpose of this paper is to apply several approaches to classify motor imageries originating from the brain in a more robust manner. For this study, dataset II from BCI competition III was used. To extract features from the brain signal, discrete wavelet transform decomposition was used. Then, several classic classifiers were implemented to be utilized in the multiple classifier system, which outperforms the reported results of other proposed methods on the dataset. Also, a variety of classifier combination methods along with genetic algorithm feature selection were evaluated and compared in order to diminish classification error. Our results suggest that an ensemble system can be employed to boost EEG classification accuracy.