Cargando…

Sparse EEG/MEG source estimation via a group lasso

Non-invasive recordings of human brain activity through electroencephalography (EEG) or magnetoencelphalography (MEG) are of value for both basic science and clinical applications in sensory, cognitive, and affective neuroscience. Here we introduce a new approach to estimating the intra-cranial sour...

Descripción completa

Detalles Bibliográficos
Autores principales:	Lim, Michael, Ales, Justin M., Cottereau, Benoit R., Hastie, Trevor, Norcia, Anthony M.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2017
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467834/ https://www.ncbi.nlm.nih.gov/pubmed/28604790 http://dx.doi.org/10.1371/journal.pone.0176835

Ejemplares similares

Groupyr: Sparse Group Lasso in Python
por: Richie-Halford, Adam, et al.
Publicado: (2021)

Sparse Damage Detection with Complex Group Lasso and Adaptive Complex Group Lasso
por: Dimopoulos, Vasileios, et al.
Publicado: (2022)

Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso
por: Wang, Jin-Jia, et al.
Publicado: (2015)

Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent
por: Klosa, Jan, et al.
Publicado: (2020)

Identification of hospital cost drivers using sparse group lasso
por: Swierkowski, Piotr, et al.
Publicado: (2018)

Genetic Variants Detection Based on Weighted Sparse Group Lasso
por: Che, Kai, et al.
Publicado: (2020)

Sensitivity of MEG and EEG to Source Orientation
por: Ahlfors, Seppo P., et al.
Publicado: (2010)

Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks
por: Dinh, Christoph, et al.
Publicado: (2021)

MEG/EEG Group Analysis With Brainstorm
por: Tadel, François, et al.
Publicado: (2019)

Regularized group regression methods for genomic prediction: Bridge, MCP, SCAD, group bridge, group lasso, sparse group lasso, group MCP and group SCAD
por: Ogutu, Joseph O, et al.
Publicado: (2014)

Spatial fidelity of MEG/EEG source estimates: A general evaluation approach
por: Samuelsson, John G., et al.
Publicado: (2020)

Statistical learning with sparsity: the lasso and generalizations
por: Hastie, Trevor, et al.
Publicado: (2015)

Group Guided Fused Laplacian Sparse Group Lasso for Modeling Alzheimer's Disease Progression
por: Liu, Xiaoli, et al.
Publicado: (2020)

Retraction of: Overcoming the inadaptability of sparse group lasso for data with various group structures by stacking
Publicado: (2023)

Validating EEG, MEG and Combined MEG and EEG Beamforming for an Estimation of the Epileptogenic Zone in Focal Cortical Dysplasia
por: Neugebauer, Frank, et al.
Publicado: (2022)

Combined EEG/MEG Can Outperform Single Modality EEG or MEG Source Reconstruction in Presurgical Epilepsy Diagnosis
por: Aydin, Ümit, et al.
Publicado: (2015)

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction
por: Bao, Forrest Sheng, et al.
Publicado: (2011)

MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG
por: Dalal, Sarang S., et al.
Publicado: (2011)

MobilePrune: Neural Network Compression via ℓ(0) Sparse Group Lasso on the Mobile System
por: Shao, Yubo, et al.
Publicado: (2022)

A Novel Approach to Estimating the Cortical Sources of Sleep Spindles Using Simultaneous EEG/MEG
por: Mylonas, Dimitrios, et al.
Publicado: (2022)

Multi-Task Learning for Compositional Data via Sparse Network Lasso
por: Okazaki, Akira, et al.
Publicado: (2022)

Editorial: From Raw MEG/EEG to Publication: How to Perform MEG/EEG Group Analysis With Free Academic Software
por: Delorme, Arnaud, et al.
Publicado: (2022)

Robust Adaptive Lasso method for parameter’s estimation and variable selection in high-dimensional sparse models
por: Wahid, Abdul, et al.
Publicado: (2017)

EEG/MEG Source Imaging: Methods, Challenges, and Open Issues
por: Wendel, Katrina, et al.
Publicado: (2009)

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM()
por: López, J.D., et al.
Publicado: (2014)

Source Reconstruction Accuracy of MEG and EEG Bayesian Inversion Approaches
por: Belardinelli, Paolo, et al.
Publicado: (2012)

Accounting for Linear Transformations of EEG and MEG Data in Source Analysis
por: Hipp, Joerg F., et al.
Publicado: (2015)

Adaptive cortical parcellations for source reconstructed EEG/MEG connectomes
por: Farahibozorg, Seyedeh-Rezvan, et al.
Publicado: (2018)

Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity
por: van Mierlo, Pieter, et al.
Publicado: (2019)

Image Segmentation Based on Relative Motion and Relative Disparity Cues in Topographically Organized Areas of Human Visual Cortex
por: Kohler, Peter J., et al.
Publicado: (2019)

Use of Overlapping Group LASSO Sparse Deep Belief Network to Discriminate Parkinson's Disease and Normal Control
por: Shen, Ting, et al.
Publicado: (2019)

Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on fMRI Dataset
por: Li, Yao, et al.
Publicado: (2020)

Correction: Accounting for Linear Transformations of EEG and MEG Data in Source Analysis
Publicado: (2015)

EEG/MEG Signal Processing
por: Cichocki, A., et al.
Publicado: (2007)

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI
por: Gulraiz, Asma, et al.
Publicado: (2022)

Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models
por: Paz-Linares, Deirel, et al.
Publicado: (2017)

Corrigendum: Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on fMRI Dataset
por: Li, Yao, et al.
Publicado: (2020)

Practical constraints on estimation of source extent with MEG beamformers
por: Hillebrand, Arjan, et al.
Publicado: (2011)

Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG
por: Krishnaswamy, Pavitra, et al.
Publicado: (2017)

Dynamic causal modelling for EEG and MEG
por: Kiebel, Stefan J., et al.
Publicado: (2008)

Cannot write session to /tmp/vufind_sessions/sess_fn7ad7p5105fesa8r8dfo6knq9