Less Is Enough: Assessment of the Random Sampling Method for the Analysis of Magnetoencephalography (MEG) Data

Magnetoencephalography (MEG) aims at reconstructing the unknown neuroelectric activity in the brain from non-invasive measurements of the magnetic field induced by neural sources. The solution of this ill-posed, ill-conditioned inverse problem is usually dealt with using regularization techniques that are often time-consuming, and computationally and memory storage demanding. In this paper we analyze how a slimmer procedure, random sampling, affects the estimation of the brain activity generated by both synthetic and real sources.

[1]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[2]  Olaf Hauk,et al.  Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics , 2011, NeuroImage.

[3]  Krish D. Singh,et al.  A new approach to neuroimaging with magnetoencephalography , 2005, Human brain mapping.

[4]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[5]  Annalisa Pascarella,et al.  An inversion method based on random sampling for real-time MEG neuroimaging , 2016 .

[6]  Matthew J. Brookes,et al.  A new generation of magnetoencephalography: Room temperature measurements using optically-pumped magnetometers , 2017, NeuroImage.

[7]  Seppo P. Ahlfors,et al.  Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates , 2006, NeuroImage.

[8]  Andy Haas,et al.  A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem , 2015, Brain sciences.

[9]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[10]  Francesca Pitolli,et al.  Neuroelectric source localization by random spatial sampling , 2016, J. Comput. Appl. Math..

[11]  Nathan Halko,et al.  Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..

[12]  David Poeppel,et al.  Performance of an MEG adaptive-beamformer technique in the presence of correlated neural activities: effects on signal intensity and time-course estimates , 2002, IEEE Transactions on Biomedical Engineering.

[13]  E. Halgren,et al.  Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity , 2000, Neuron.

[14]  Xavier Tricoche,et al.  Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling , 2006, NeuroImage.

[15]  Byoung-Kyong Min,et al.  Neuroimaging-based approaches in the brain-computer interface. , 2010, Trends in biotechnology.

[16]  M. Hämäläinen,et al.  Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data , 1989, IEEE Transactions on Biomedical Engineering.

[17]  Matti Stenroos,et al.  Measuring MEG closer to the brain: Performance of on-scalp sensor arrays , 2016 .

[18]  R. Leahy,et al.  EEG and MEG: forward solutions for inverse methods , 1999, IEEE Transactions on Biomedical Engineering.

[19]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[20]  Polina Golland,et al.  A distributed spatio-temporal EEG/MEG inverse solver , 2009, NeuroImage.

[21]  Lauri Parkkonen.  Real-Time Magnetoencephalography for Neurofeedback and Closed-Loop Experiments , 2015 .

[22]  Michael A. Saunders,et al.  LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares , 1982, TOMS.

[23]  関原 謙介,et al.  Adaptive Spatial Filters for Electromagnetic Brain Imaging , 2008 .

[24]  Richard M. Leahy,et al.  BrainSuite: An Automated Cortical Surface Identification Tool , 2000, MICCAI.

[25]  Martin Luessi,et al.  MNE software for processing MEG and EEG data , 2014, NeuroImage.

[26]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[27]  A. Walker.  Electroencephalography, Basic Principles, Clinical Applications and Related Fields , 1982 .

[28]  L. Parkkonen,et al.  Implementation of a beam forming technique in real-time magnetoencephalography. , 2013, Journal of integrative neuroscience.

[29]  Wei Wang,et al.  rtMEG: A Real-Time Software Interface for Magnetoencephalography , 2011, Comput. Intell. Neurosci..

[30]  Lutz Trahms,et al.  Optically Pumped Magnetometers for MEG , 2019, Magnetoencephalography.

[31]  Martin Luessi,et al.  Real-Time MEG Source Localization Using Regional Clustering , 2015, Brain Topography.

[32]  Xin Li,et al.  Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies , 2011 .

[33]  Daniela Calvetti,et al.  Hierarchical beamformer and cross-talk reduction in electroneurography , 2011, Journal of neural engineering.

[34]  A. Gramfort,et al.  Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods , 2012, Physics in medicine and biology.