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Artifact suppression and analysis of brain activities with electroencephalography signals

Neural Regeneration Research 2013年第16期8卷 1500-1513页

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

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.

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

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