NDLI: Minimum Variance Brain Source Localization for Short Data Sequences

Content Provider	IEEE Xplore Digital Library
Author	Ravan, M. Reilly, J.P. Hasey, G.
Copyright Year	1964
Abstract	In the electroencephalogram (EEG) or magnetoencephalogram (MEG) context, brain source localization methods that rely on estimating second-order statistics often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. Due to the correlated background EEG/MEG signal, an adaptive approach to localization is desirable. Previous work has addressed these issues by reducing the adaptive degrees of freedom (DoFs). This reduction results in decreased resolution and accuracy of the estimated source configuration. This paper develops and tests a new multistage adaptive processing technique based on the minimum variance beamformer for brain source localization that has been previously used in the radar statistical signal processing context. This processing, referred to as the fast fully adaptive (FFA) approach, can significantly reduce the required sample support, while still preserving all available DoFs. To demonstrate the performance of the FFA approach in the limited data scenario, simulation and experimental results are compared with two previous beamforming approaches; i.e., the fully adaptive minimum variance beamforming method and the beamspace beamforming method. Both simulation and experimental results demonstrate that the FFA method can localize all types of brain activity more accurately than the other approaches with limited data.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	12
File Size	663218
Starting Page	535
Ending Page	546
File Format	PDF
ISSN	00189294
Volume Number	61
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Noise Electroencephalography Brain modeling Electrodes Array signal processing Electric potential magnetoencephalography (MEG) Beamforming brain source localization electroencephalography (EEG) evoked potentials fast fully adaptive (FFA) processing
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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