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  Online distributed source localization from EEG/MEG data

Pieloth, C., Knösche, T. R., Maess, B., & Fuchs, M. (2014). Online distributed source localization from EEG/MEG data. International Journal of Computing, 13(1), 17-24.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-07E8-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-14ED-C
Genre: Journal Article

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 Creators:
Pieloth, Christof1, Author
Knösche, Thomas R.2, Author              
Maess, Burkhard2, Author              
Fuchs, Mirco1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Methods and Development Group MEG and EEG - Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_2205650              

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Free keywords: EEG; MEG; Source Imaging; GPGPU
 Abstract: Electroencephalography (EEG) and Magnetoencephalography (MEG) provide insight into neuronal processes in the brain in a real-time scale. This renders these modalities particularly interesting for online analysis methods, e.g. to visualize brain activity in real-time. Brain activity can be modeled in terms of a source distribution found by solving the bioelectromagnetic inverse problem, e.g. using linear source reconstruction methods. Such methods are particularly suitable to be used on modern highly parallel processing systems, such as widely available graphic processing units (GPUs). We present a system that, according to its modular scheme, can be configured in a very flexible way using graphical building blocks. Different preprocessing algorithms together with a linear source reconstruction method can be used for online analysis. The algorithms use both CPU and GPU resources. We tested our system in a simulation and in a realistic experiment.

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Language(s): eng - English
 Dates: 2014-08
 Publication Status: Published in print
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Title: International Journal of Computing
Source Genre: Journal
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Publ. Info: Ternopil, Ukraine : TNEU
Pages: - Volume / Issue: 13 (1) Sequence Number: - Start / End Page: 17 - 24 Identifier: ISSN: 1727-6209
CoNE: /journals/resource/1727-6209