Hsu Wei-Yen
Department of Information Management, National Chung Cheng University, Chiayi County, Taiwan Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi County, Taiwan
Clin EEG Neurosci. 2015 Apr;46(2):113-8. doi: 10.1177/1550059414555123. Epub 2014 Nov 16.
An electroencephalogram recognition system considering phase features is proposed to enhance the performance of motor imagery classification in this study. It mainly consists of feature extraction, feature selection and classification. Surface Laplacian filter is used for background removal. Several potential features, including phase features, are then extracted to enhance the classification accuracy. Next, genetic algorithm is used to select sub-features from feature combination. Finally, selected features are classified by extreme learning machine. Compared with "without phase features" and linear discriminant analysis on motor imagery data from 2 data sets, the results denote that the proposed system achieves enhanced performance, which is suitable for the brain-computer interface applications.
本研究提出了一种考虑相位特征的脑电图识别系统，以提高运动想象分类的性能。它主要由特征提取、特征选择和分类组成。使用表面拉普拉斯滤波器去除背景。然后提取包括相位特征在内的几个潜在特征，以提高分类准确率。接下来，使用遗传算法从特征组合中选择子特征。最后，通过极限学习机对所选特征进行分类。与来自2个数据集的运动想象数据上的“无相位特征”和线性判别分析相比，结果表明所提出的系统实现了性能提升，适用于脑机接口应用。
