NDLI: Electromagnetic tomography via source-space-ICA

Content Provider	IEEE Xplore Digital Library
Author	Jonmohamadi, Y. Poudel, G. Innes, C. Jones, R.D.
Copyright Year	2013
Description	Author affiliation: New Zealand Brain Res. Inst., Christchurch, New Zealand (Poudel, G.) \|\| Dept. of Med. Phys. & Bioeng., Christchurch Hosp., Christchurch, New Zealand (Innes, C.; Jones, R.D.) \|\| Dept. of Med., Univ. of Otago, Christchurch, New Zealand (Jonmohamadi, Y.)
Abstract	We propose a technique, called source-space-ICA to provide spatiotemporal reconstruction of brain sources. First, the weight-vector-normalized minimum variance beamformer is applied to reconstruct the electrical activity of a 3D scanning grid which covers the whole brain. Second, principal component analysis is used to reduce the dimension of the reconstructed signal matrix of the source-space, then independent component analysis (ICA) is applied on the resulting matrix to identify multiple signal sources in the source-space. Third, the demixing weight vectors obtained by ICA for the identified independent components are projected back into the SS to obtain tomographic maps of the sources. Besides localization, the proposed source-space-ICA approach reconstructs the time-course of each source in a single time-series without requiring prior knowledge of location, orientation, and number of sources for a given segment of EEG/MEG. Simulated EEG was used to evaluate the source-space-ICA. The results show that the source-space-ICA approach is able to separate and localize multiple weak sources and is robust to interference from other sources as it identifies the sources based on their statistical independence.
Starting Page	37
Ending Page	40
File Size	541791
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609431
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Tomography Vectors Image reconstruction Electromagnetics Principal component analysis Signal to noise ratio
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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