EEG extended source localization: Tensor-based vs. conventional methods

The localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2014-08, Vol.96, p.143-157
Main Authors: Becker, H., Albera, L., Comon, P., Haardt, M., Birot, G., Wendling, F., Gavaret, M., Bénar, C.G., Merlet, I.
Format: Article
Language:English
Subjects:
Algorithms
Bioengineering
Biological and medical sciences
Brain - physiology
Brain Mapping - methods
Computer Science
Computer Simulation
Decomposition
Distributed source localization
EEG
Electroencephalography - methods
Epilepsy
Fourier transforms
Fundamental and applied biological sciences. Psychology
Humans
Life Sciences
Methods
Models, Neurological
Music
Nerve Net - physiology
Neurons and Cognition
Noise
Reproducibility of Results
Sensitivity and Specificity
Signal and Image Processing
Signal-To-Noise Ratio
Space–time–frequency analysis
Space–time–wave–vector analysis
Tensor decomposition
Tomography
Vertebrates: nervous system and sense organs
Wavelet transforms
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Summary:The localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and low signal-to-noise ratios. To overcome these problems, tensor-based preprocessing can be applied, which consists in constructing a space–time–frequency (STF) or space–time–wave–vector (STWV) tensor and decomposing it using the Canonical Polyadic (CP) decomposition. In this paper, we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition. Furthermore, we conduct a detailed study of the tensor-based preprocessing methods, including an analysis of their theoretical foundation, their computational complexity, and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sLORETA, cortical LORETA (cLORETA), and 4-ExSo-MUSIC. Our objective consists, on the one hand, in demonstrating the gain in performance that can be achieved by tensor-based preprocessing, and, on the other hand, in pointing out the limits and drawbacks of this method. Finally, we validate the STF and STWV techniques on real measurements to demonstrate their usefulness for practical applications. •Localization of spatially distributed sources using tensor-based preprocessing•Performance analysis of two tensor-based preprocessing methods•Numerical complexity of tensor-based and conventional algorithms•Performance comparison on realistic simulated EEG data•Validation on real EEG data from an epilepsy patient
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2014.03.043