Artifact suppression and analysis of brain activities with electroencephalography signals

作     者：Md. Rashed-Al-Mahfuz Md. Rabiul Islam Keikichi Hirose Md. Khademul Islam Molla 

作者机构：Department of Computer Science and Engineering Pabna University of Science and Technology Department of Information and Communication Engineering The University of Tokyo Department of Computer Science and Engineering Rajshahi University 

出 版 物：《Neural Regeneration Research》 (中国神经再生研究（英文版）)

年 卷 期：2013年第8卷第16期

页      面：1500-1513页

核心收录：

学科分类：0711[理学-系统科学] 1001[医学-基础医学(可授医学、理学学位)] 07[理学] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 10[医学] 

基　　金：supported by a grant from the National Institute of Information and Communications Technology(NICT) Japan 

主　　题：neural regeneration brain activity brain waves data adaptive filtering electroencephalography electro-oculogram artifact topographic mapping Wiener filtering neuroregeneration 

摘      要：Brain-computer interface is a communication system that connects the brain with computer （or other devices） but is not dependent on the normal output of the brain （i.e., peripheral nerve and muscle）. Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This paper presented a data adaptive technique for artifact suppression and brain wave extraction from electroencephalography signals to detect regional brain activities. Empirical mode decomposition based adaptive thresholding approach was employed here to suppress the electro-oculogram artifact. Fractional Gaussian noise was used to determine the threshold level derived from the analysis data without any training. The purified electroencephalography signal was composed of the brain waves also called rhythmic components which represent the brain activities. The rhythmic components were extracted from each electroencephalography channel using adaptive wiener filter with the original scale. The regional brain activities were mapped on the basis of the spatial distribution of rhythmic components, and the results showed that different regions of the brain are activated in response to different stimuli. This research analyzed the activities of a single rhythmic component, alpha with respect to different motor imaginations. The experimental results showed that the proposed method is very efficient in artifact suppression and identifying individual motor imagery based on the activities of alpha component.