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Coppieters't Wallant+Dorothe+

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See detailSleep and Electroencephalography, Preprocessing and analyses of a specific sleep EEG pattern
Coppieters't Wallant, Dorothe ULg

Doctoral thesis (2016)

Preprocessing and analyses of a specific sleep EEG pattern. Automatic detection methods, self-adjustable to individuals characteristics, were developed in order to remove artefacts from EEG sleep ... [more ▼]

Preprocessing and analyses of a specific sleep EEG pattern. Automatic detection methods, self-adjustable to individuals characteristics, were developed in order to remove artefacts from EEG sleep recordings, detect sleep spindles and analyse NREM power spectrum. Methods are described, assessed and discussed. [less ▲]

Detailed reference viewed: 29 (6 ULg)
Peer Reviewed
See detailAge-related differences in the dynamics of cortical excitability and cognitive inhibition during prolongedwakefulness
Gaggioni, Giulia ULg; Chelllappa, S.; Ly, J. et al

Conference (2016, September)

Detailed reference viewed: 33 (11 ULg)
Peer Reviewed
See detailSleep deprivation affects brain global cortical responsivenes
Gaggioni, Giulia ULg; Chellappa, S; Ly, J et al

Conference (2016, June 15)

Detailed reference viewed: 42 (7 ULg)
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Peer Reviewed
See detailAutomatic artifacts and arousals detection in whole-night sleep EEG recordings
Coppieters't Wallant, Dorothe ULg; Muto, Vincenzo ULg; Gaggioni, Giulia ULg et al

in Journal of Neuroscience Methods (2016), 258

In sleep electroencephalographic (EEG) signals, artifacts and arousals marking are usually part of the processing. This visual inspection by a human expert has two main drawbacks: it is very time ... [more ▼]

In sleep electroencephalographic (EEG) signals, artifacts and arousals marking are usually part of the processing. This visual inspection by a human expert has two main drawbacks: it is very time consuming and subjective. To detect artifacts and arousals in a reliable, systematic and reproducible automatic way, we developed an automatic detection based on time and frequency analysis with adapted thresholds derived from data themselves. The automatic detection performance is assessed using 5 statistic parameters, on 60 whole night sleep recordings coming from 35 healthy volunteers (male and female) aged between 19 and 26. The proposed approach proves its robustness against inter- and intra-, subjects and raters’ scorings, variability. The agreement with human raters is rated overall from substantial to excellent and provides a significantly more reliable method than between human raters. Existing methods detect only specific artifacts or only arousals, and/or these methods are validated on short episodes of sleep recordings, making it difficult to compare with our whole night results. The method works on a whole night recording and is fully automatic, reproducible, and reliable. Furthermore the implementation of the method will be made available online as open source code. [less ▲]

Detailed reference viewed: 81 (35 ULg)
Peer Reviewed
See detailSleep deprivation affects brain global cortical responsiveness
Gaggioni, Giulia ULg; Chellappa; Ly et al

Poster (2016)

Detailed reference viewed: 45 (2 ULg)
Peer Reviewed
See detailSleep deprivation affects global cortical responsiveness
Gaggioni, Giulia ULg; Ly, Julien; Chellappa, Sarah et al

Conference (2015, November 02)

Detailed reference viewed: 32 (3 ULg)
Peer Reviewed
See detailHuman cortical excitability depends on time awake and circadian phase
Gaggioni, Giulia ULg; Ly, Julien; Chellappa, Sarah Laxhmi ULg et al

Poster (2015, January 27)

Detailed reference viewed: 57 (16 ULg)
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Peer Reviewed
See detailAutomatic artifact detection for whole-night polysomnographic sleep recordings
Coppieters't Wallant, Dorothe ULg; Chellappa, Sarah Laxhmi ULg; Gaggioni, Giulia ULg et al

Poster (2014, September 17)

Detecting of bad channels and artifacts for whole-night polysomnographic recordings is very time consuming and tedious. We therefore developed an automatic procedure to automatize this job.

Detailed reference viewed: 58 (33 ULg)
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See detailNon invasive sleep monitoring
Deliège, Benjamin; Coppieters't Wallant, Dorothe ULg

Conference (2012, April)

Detailed reference viewed: 15 (3 ULg)
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See detailApplication des ondes acoustiques à la rhinométrie
Coppieters't Wallant, Dorothe ULg

Master's dissertation (2010)

Detailed reference viewed: 33 (12 ULg)
See detailFASST - fMRI Artefact rejection and Sleep Scoring Toolbox
Phillips, Christophe ULg; Schrouff, Jessica ULg; Coppieters't Wallant, Dorothe ULg et al

Software (2007)

"FASST" stands for "fMRI Artefact rejection and Sleep Scoring Toolbox". This M/EEG toolbox is developed by researchers from the Cyclotron Research Centre, University of Li ege, Belgium, with the financial ... [more ▼]

"FASST" stands for "fMRI Artefact rejection and Sleep Scoring Toolbox". This M/EEG toolbox is developed by researchers from the Cyclotron Research Centre, University of Li ege, Belgium, with the financial support of the Fonds de la Recherche Scienti que-FNRS, the Queen Elizabeth's funding, and the University of Li ege. On Dr. Pierre Maquet's impulse we started writing these tools to analyze our sleep EEG-fMRI data and tackle four crucial issues: * Continuous M/EEG. Long multi-channel recording of M/EEG data can be enormous. These data are cumbersome to handle as it usually involves displaying, exploring, comparing, chunking, appending data sets, etc. * EEG-fMRI. When recording EEG and fMRI data simultaneously, the EEG signal acquired contains, on top of the usual neural and ocular activity, artefacts induced by the gradient switching and high static eld of an MR scanner. The rejection of theses artefacts is not easy especially when dealing with brain spontaneous activity. * Scoring M/EEG. Reviewing and scoring continuous M/EEG recordings, such as is common with sleep recordings, is a tedious task as the scorer has to manually browse through the entire data set and give a \score" to each time-window displayed. * Waves detection. Continuous and triggerless recordings of M/EEG data show specifi c wave patterns, characteristic of the subject's state (e.g., sleep spindles or slow waves). Their automatic detection is thus important to assess those states. [less ▲]

Detailed reference viewed: 45 (12 ULg)