Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66243
Title: An EEG blind source separation algorithm based on a weak exclusion principle
Authors: Ma, L
Blu, T
Wang, WSY
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, v. 2016-October, 7590836, p. 859-862 How to cite?
Abstract: The question of how to separate individual brain and non-brain signals, mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings, is a significant problem in contemporary neuroscience. This study proposes and evaluates a novel EEG Blind Source Separation (BSS) algorithm based on a weak exclusion principle (WEP). The chief point in which it differs from most previous EEG BSS algorithms is that the proposed algorithm is not based upon the hypothesis that the sources are statistically independent. Our first step was to investigate algorithm performance on simulated signals which have ground truth. The purpose of this simulation is to illustrate the proposed algorithm's efficacy. The results show that the proposed algorithm has good separation performance. Then, we used the proposed algorithm to separate real EEG signals from a memory study using a revised version of Sternberg Task. The results show that the proposed algorithm can effectively separate the non-brain and brain sources.
Description: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, US, 16-20 August 2016
URI: http://hdl.handle.net/10397/66243
ISBN: 9781457702204
ISSN: 1557-170X
DOI: 10.1109/EMBC.2016.7590836
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