Cargando…

MEG and EEG data analysis with MNE-Python

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statisti...

Descripción completa

Detalles Bibliográficos
Autores principales:	Gramfort, Alexandre, Luessi, Martin, Larson, Eric, Engemann, Denis A., Strohmeier, Daniel, Brodbeck, Christian, Goj, Roman, Jas, Mainak, Brooks, Teon, Parkkonen, Lauri, Hämäläinen, Matti
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2013
Materias:	Neuroscience
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3872725/ https://www.ncbi.nlm.nih.gov/pubmed/24431986 http://dx.doi.org/10.3389/fnins.2013.00267

Ejemplares similares

A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices
por: Jas, Mainak, et al.
Publicado: (2018)

Intracranial Electrode Location and Analysis in MNE-Python
por: Rockhill, Alexander P., et al.
Publicado: (2022)

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis
por: Appelhoff, Stefan, et al.
Publicado: (2019)

Target of selective auditory attention can be robustly followed with MEG
por: Kurmanavičiūtė, Dovilė, et al.
Publicado: (2023)

On electromagnetic head digitization in MEG and EEG
por: Jaiswal, Amit, et al.
Publicado: (2023)

MEG-BIDS, the brain imaging data structure extended to magnetoencephalography
por: Niso, Guiomar, et al.
Publicado: (2018)

Across-subject offline decoding of motor imagery from MEG and EEG
por: Halme, Hanna-Leena, et al.
Publicado: (2018)

Publisher Correction: Across-subject offline decoding of motor imagery from MEG and EEG
por: Halme, Hanna-Leena, et al.
Publicado: (2018)

Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design
por: Iivanainen, Joonas, et al.
Publicado: (2021)

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

Requirements for Coregistration Accuracy in On-Scalp MEG
por: Zetter, Rasmus, et al.
Publicado: (2018)

Comparison of beamformer implementations for MEG source localization
por: Jaiswal, Amit, et al.
Publicado: (2020)

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)

Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data
por: Neymotin, Samuel A, et al.
Publicado: (2020)

A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies
por: Puce, Aina, et al.
Publicado: (2017)

Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study
por: Haumann, Niels Trusbak, et al.
Publicado: (2016)

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

Evidence for morphological composition in compound words using MEG
por: Brooks, Teon L., et al.
Publicado: (2015)

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

Permutation Statistics for Connectivity Analysis between Regions of Interest in EEG and MEG Data
por: Mamashli, Fahimeh, et al.
Publicado: (2019)

MEG language mapping using a novel automatic ECD algorithm in comparison with MNE, dSPM, and DICS beamformer
por: Babajani-Feremi, Abbas, et al.
Publicado: (2023)

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

Optical Co-registration of MRI and On-scalp MEG
por: Zetter, Rasmus, et al.
Publicado: (2019)

Comparing Features for Classification of MEG Responses to Motor Imagery
por: Halme, Hanna-Leena, et al.
Publicado: (2016)

Extended Signal-Space Separation Method for Improved Interference Suppression in MEG
por: Helle, Liisa, et al.
Publicado: (2021)

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

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

Adaptive neural network classifier for decoding MEG signals
por: Zubarev, Ivan, et al.
Publicado: (2019)

Potential of on‐scalp MEG: Robust detection of human visual gamma‐band responses
por: Iivanainen, Joonas, et al.
Publicado: (2019)

And the subsidiary lives on: Harnessing complex realities in the contemporary MNE
por: Andrews, Daniel S., et al.
Publicado: (2022)

Non-linear canonical correlation for joint analysis of MEG signals from two subjects
por: Campi, Cristina, et al.
Publicado: (2013)

Neural signatures of hand kinematics in leaders vs. followers: A dual-MEG study()
por: Zhou, Guangyu, et al.
Publicado: (2016)

On-scalp MEG system utilizing an actively shielded array of optically-pumped magnetometers
por: Iivanainen, Joonas, et al.
Publicado: (2019)

rtMEG: A Real-Time Software Interface for Magnetoencephalography
por: Sudre, Gustavo, et al.
Publicado: (2011)

International connectedness and local disconnectedness: MNE strategy, city-regions and disruption
por: Lorenzen, Mark, et al.
Publicado: (2020)

A Lightweight YOLOv5-MNE Algorithm for SAR Ship Detection
por: Pang, Lei, et al.
Publicado: (2022)

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

MEG Source Localization Using Invariance of Noise Space
por: Zhang, Junpeng, et al.
Publicado: (2013)

Navigating MNE control and coordination: A critical review and directions for future research
por: Zeng, Rong (Ratchel), et al.
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)

Cannot write session to /tmp/vufind_sessions/sess_231g6c35usg6mier0h09dqmeus