EEG

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

For a high-level presentation of the main findings, please consider this Twitter thread: Excited to share our paper @NeuroImage_EiC by @DavSabbagh with @PierreAblin @GaelVaroquaux @agramfort https://t.co/ke0wO8aqwk Nonlinear subject-level regression on M/EEG using linear models without source localization: theory + empirical benchmarks Thread👇 pic.twitter.com/0vevsO3UmG — Denis A. Engemann (@dngman) May 18, 2020

Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness

MNE: Software for Acquiring, Processing,and Visualizing MEG/EEG Data

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Robust EEG-based cross-site and cross-protocol classification of states of consciousness

For a discussion of the neurophysiologicla implications, please consider the companion article by Rodika Sokoliuk and Damian Cruse Should you have no time to read the paper, this Twitter thread gives you the gist: 1/ New paper out! @Brain1878 https://t.co/l3VIfg4s9F Robust #MachineLearning of diagnosis from #EEG in disorders of #consciousness powered by @CEAParisSaclay @cdf1530 @Coma_Science @dc_uba @ERC_Research @Inria_Saclay @Inserm @mne_news @NeuroSpin_91 @icm_institute @scikit_learn pic.twitter.com/JsDMUYWw6C — Denis A. Engemann (@dngman) October 4, 2018

Survival and consciousness recovery are better in the minimally conscious state than in the vegetative state

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Encoding and Decoding Neuronal Dynamics: Methodological Framework to Uncover the Algorithms of Cognition

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A reproducible MEG/EEG group study with the MNE software: recommendations, quality assessments, and good practices

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Autoreject: Automated artifact rejection for MEG and EEG data

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Brain–heart interactions reveal consciousness in noncommunicating patients

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Automated rejection and repair of bad trials in MEG/EEG

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