A performance study of various brain source imaging approaches

The objective of brain source imaging consists in reconstructing the cerebral activity everywhere within the brain based on EEG or MEG measurements recorded on the scalp. This requires solving an ill-posed linear inverse problem. In order to restore identifiability, additional hypotheses need to be imposed on the source distribution, giving rise to an impressive number of brain source imaging algorithms. However, a thorough comparison of different methodologies is still missing in the literature. In this paper, we provide an overview of priors that have been used for brain source imaging and conduct a comparative simulation study with seven representative algorithms corresponding to the classes of minimum norm, sparse, tensor-based, subspace-based, and Bayesian approaches. This permits us to identify new benchmark algorithms and promising directions for future research.

[1]  Fumikazu Miwakeichi,et al.  Decomposing EEG data into space–time–frequency components using Parallel Factor Analysis , 2004, NeuroImage.

[2]  Lei Ding,et al.  Reconstructing cortical current density by exploring sparseness in the transform domain , 2009, Physics in medicine and biology.

[3]  Laurent Albera,et al.  Localization of extended brain sources from EEG/MEG: The ExSo-MUSIC approach , 2011, NeuroImage.

[4]  Michael Falkenstein Editor-in-Chief.  The Journal of Psychophysiology , 2008 .

[5]  Fabrice Wendling,et al.  A Physiologically Plausible Spatio-Temporal Model for EEG Signals Recorded With Intracerebral Electrodes in Human Partial Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[6]  Richard M. Leahy,et al.  Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..

[7]  Julia P. Owen,et al.  Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG , 2010, NeuroImage.

[8]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[9]  Bart Vanrumste,et al.  Journal of Neuroengineering and Rehabilitation Open Access Review on Solving the Inverse Problem in Eeg Source Analysis , 2022 .

[10]  Jens Haueisen,et al.  Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations , 2013, NeuroImage.

[11]  P. Comon,et al.  Tensor decompositions, alternating least squares and other tales , 2009 .

[12]  Laurent Albera,et al.  Multi-way space-time-wave-vector analysis for EEG source separation , 2012, Signal Process..

[13]  E. Somersalo,et al.  Visualization of Magnetoencephalographic Data Using Minimum Current Estimates , 1999, NeuroImage.

[14]  Joachim Möcks,et al.  Decomposing event-related potentials: A new topographic components model , 1988, Biological Psychology.

[15]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[16]  F Wendling,et al.  EEG extended source localization: Tensor-based vs. conventional methods , 2014, NeuroImage.

[17]  Alexandre Gramfort,et al.  Mapping, timing and tracking cortical activations with MEG and EEG: Methods and application to human vision. (Localisation et suivi d'activité fonctionnelle cérébrale en électro et magnétoencéphalographie: Méthodes et applications au système visuel humain) , 2009 .

[18]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[19]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[20]  Polina Golland,et al.  A Distributed Spatio-temporal EEG/MEG Inverse Solver , 2008, MICCAI.

[21]  David P. Wipf,et al.  A unified Bayesian framework for MEG/EEG source imaging , 2009, NeuroImage.

[22]  R. Pascual-Marqui.  Review of methods for solving the EEG inverse problem , 1999 .

[23]  C. Im,et al.  Evaluation of Algorithms for Intracranial EEG (iEEG) Source Imaging of Extended Sources: Feasibility of Using iEEG Source Imaging for Localizing Epileptogenic Zones in Secondary Generalized Epilepsy , 2011, Brain Topography.