Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning

by Lee, M, Sanz, L, Barra, A, Wolff, A, Nieminen, J, Boly, M, Rosanova, M, Casarotto, S, Bodart, O, Annen, J, Thibaut, A, Panda, R, Bonhomme, V, Massimini, M, Tononi, G, Laureys, S, Gosseries, O and Lee, SW

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Reference:

Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning (Lee, M, Sanz, L, Barra, A, Wolff, A, Nieminen, J, Boly, M, Rosanova, M, Casarotto, S, Bodart, O, Annen, J, Thibaut, A, Panda, R, Bonhomme, V, Massimini, M, Tononi, G, Laureys, S, Gosseries, O and Lee, SW), In Nature Communications, volume 13, 2022.

Bibtex Entry:

@article{MLEE2022,
    title = {Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning},
    journal = {Nature Communications},
    year = {2022},
    volume = {13},
    NUMBER = {1},
    pages = {1064},
    doi = {10.1038/s41467-022-28451-0},
    url = {https://www.nature.com/articles/s41467-022-28451-0.pdf},
    author = {Lee, M and Sanz, L and Barra, A and Wolff, A and Nieminen, J and Boly, M and Rosanova, M and Casarotto, S and Bodart, O and Annen, J and Thibaut, A and Panda, R and Bonhomme, V and Massimini, M and Tononi, G and Laureys, S and Gosseries, O and Lee, SW},
    keywords = {IIT, integrated information theory, EEG, TMS, measuring consciousness, REM, ketamine, anesthesia, arousal, awareness, ECI, deep learning},
}