Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals
暂无分享,去创建一个
[1] A. N. Tikhonov,et al. Solutions of ill-posed problems , 1977 .
[2] R D Pascual-Marqui,et al. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.
[3] E. Somersalo,et al. Visualization of Magnetoencephalographic Data Using Minimum Current Estimates , 1999, NeuroImage.
[4] John C. Mosher,et al. Anatomically and Functionally Constrained Minimum-Norm Estimates , 2010 .
[5] R. Ilmoniemi,et al. Signal-space projection method for separating MEG or EEG into components , 1997, Medical and Biological Engineering and Computing.
[6] Nathan Halko,et al. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..
[7] A. Gramfort,et al. Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods , 2012, Physics in medicine and biology.
[8] J.C. Mosher,et al. Recursive MUSIC: A framework for EEG and MEG source localization , 1998, IEEE Transactions on Biomedical Engineering.
[9] Christopher M. Bishop,et al. Bayesian PCA , 1998, NIPS.
[10] Seungjin Choi,et al. Independent Component Analysis , 2009, Handbook of Natural Computing.
[11] Joachim Gross,et al. Good practice for conducting and reporting MEG research , 2013, NeuroImage.
[12] Lukas Breuer,et al. A new constrained ICA approach for optimal signal decomposition in polarized light imaging , 2013, Journal of Neuroscience Methods.
[13] E. Halgren,et al. Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity , 2000, Neuron.
[14] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[15] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[16] Matti Stenroos,et al. A framework for the design of flexible cross‐talk functions for spatial filtering of EEG/MEG data: DeFleCT , 2014, Human brain mapping.
[17] R. M. Cormack,et al. A Review of Classification , 1971 .
[18] Jens Haueisen,et al. Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations , 2013, NeuroImage.
[19] Karl J. Friston,et al. EEG and MEG Data Analysis in SPM8 , 2011, Comput. Intell. Neurosci..
[20] Kensuke Sekihara,et al. A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data , 2007, Statistics in medicine.
[21] Z. Yao,et al. Discriminative analysis with a limited number of MEG trials in depression. , 2014, Journal of affective disorders.
[22] Arthur E. Hoerl,et al. Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.
[23] G. Pfurtscheller,et al. Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[24] C. Chui,et al. Article in Press Applied and Computational Harmonic Analysis a Randomized Algorithm for the Decomposition of Matrices , 2022 .
[25] David Barber,et al. Bayesian reasoning and machine learning , 2012 .
[26] J. Haxby,et al. Human neural systems for face recognition and social communication , 2002, Biological Psychiatry.
[27] R. Ilmoniemi,et al. Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .
[28] Uwe Pietrzyk,et al. Integration of Amplitude and Phase Statistics for Complete Artifact Removal in Independent Components of Neuromagnetic Recordings , 2008, IEEE Transactions on Biomedical Engineering.
[29] Anders M. Dale,et al. Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain , 2004, NeuroImage.
[30] L. Breiman,et al. Submodel selection and evaluation in regression. The X-random case , 1992 .
[31] Seppo P. Ahlfors,et al. Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates , 2006, NeuroImage.
[32] R. Henson,et al. Neural response suppression, haemodynamic repetition effects, and behavioural priming , 2003, Neuropsychologia.
[33] Michael Eickenberg,et al. Machine learning for neuroimaging with scikit-learn , 2014, Front. Neuroinform..
[34] Hagai Attias,et al. Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data , 2008, NeuroImage.
[35] D M Durand,et al. Suppression of axonal conduction by sinusoidal stimulation in rat hippocampus in vitro , 2007, Journal of neural engineering.
[36] W. Drongelen,et al. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.
[37] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[38] Martin Luessi,et al. MNE software for processing MEG and EEG data , 2014, NeuroImage.
[39] Galit Yovel,et al. Face recognition systems in monkey and human: are they the same thing? , 2013, F1000prime reports.
[40] Tom Minka,et al. Automatic Choice of Dimensionality for PCA , 2000, NIPS.
[41] Stephen M. Smith,et al. Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.
[42] Olivier Ledoit,et al. A well-conditioned estimator for large-dimensional covariance matrices , 2004 .
[43] S. Taulu,et al. Applications of the signal space separation method , 2005, IEEE Transactions on Signal Processing.
[44] Fetsje Bijma,et al. The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products , 2005, NeuroImage.
[45] San Cristóbal Mateo,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996 .
[46] Alfred O. Hero,et al. Shrinkage Algorithms for MMSE Covariance Estimation , 2009, IEEE Transactions on Signal Processing.
[47] Hagai Attias,et al. A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data , 2006, NeuroImage.
[48] Mark W. Woolrich,et al. MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularization , 2011, NeuroImage.