Localization of abnormal EEG sources using blind source separation partially constrained by the locations of known sources

Electroencephalogram (EEG) source localization requires a solution to an ill-posed inverse problem. The additional challenge is to solve this problem in the context of multiple moving sources. An effective and simple technique for both separation and localization of EEG sources is therefore proposed...

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Bibliographic Details
Main Authors: Mohamed Amin Latif, Saeid Sanei, Jonathon Chambers, Leor Shoker
Format: Article
Language:English
Subjects:
Blind source separation (BSS)
Electroencephalogram (EEG)
Mechanical engineering not elsewhere classified
Partially constrained
Source localization
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Summary:Electroencephalogram (EEG) source localization requires a solution to an ill-posed inverse problem. The additional challenge is to solve this problem in the context of multiple moving sources. An effective and simple technique for both separation and localization of EEG sources is therefore proposed by incorporating an algorithmically coupled blind source separation (BSS) approach. The method relies upon having a priori knowledge of the locations of a subset of the sources. The cost function of the BSS algorithm is constrained by this information, and the unknown sources are iteratively calculated. An important application of this method is to localize abnormal sources, which, for example, cause changes in attention, movement, and behavior. In this application, the Alpha rhythm was considered as the known sources. Simulation studies are presented to support the potential of the approach in terms of source localization.