PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combining sparsity and rotational invariance in EEG/MEG source reconstruction
Stefan Haufe, Vadim Nikulin, Klaus-Robert Müller, Andreas Ziehe and Guido Nolte
NeuroImage Volume 42, Number 2, pp. 726-738, 2008.

Abstract

We introduce Focal Vector Field Reconstruction (FVR), a novel technique for the inverse imaging of vector fields. The method was designed to simultaneously achieve two goals: a) invariance with respect to the orientation of the coordinate system, and b) a preference for sparsity of the solutions and their spatial derivatives. This was achieved by defining the regulating penalty function, which renders the solutions unique, as a global ℓ1-norm of local ℓ2-norms. We show that the method can be successfully used for solving the EEG inverse problem. In the joint localization of 2–3 simulated dipoles, FVR always reliably recovers the true sources. The competing methods have limitations in distinguishing close sources because their estimates are either too smooth (LORETA, Minimum ℓ2-norm) or too scattered (Minimum ℓ1-norm). In both noiseless and noisy simulations, FVR has the smallest localization error according to the Earth Mover's Distance (EMD), which is introduced here as a meaningful measure to compare arbitrary source distributions. We also apply the method to the simultaneous localization of left and right somatosensory N20 generators from real EEG recordings. Compared to its peers FVR was the only method that delivered correct location of the source in the somatosensory area of each hemisphere in accordance with neurophysiological prior knowledge.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Brain Computer Interfaces
ID Code:4264
Deposited By:Stefan Haufe
Deposited On:07 February 2009