, 144 6.1.4.2 Fréquence de l'activité manuelle par rapport à la performance BCI145 6.1.4.3 La prise en compte d'un état de repos est-elle pertinente pour une BCI ?

.. .. Conclusion,

.. .. Vers-un-meilleur-apprentissage-de-l'imagination-motrice-kinesthésique,

, 156 6.2.4.1 Grasp'it : une BCI pour aider à l'apprentissage de la KMI

. .. Conclusion-du-chapitre,

, Chapitre 7

, Détection de l'activité cérébrale motrice sous l'effet d'un anesthésiant 161

. .. Effet, 1.2.2 Comité d'éthique et enregistrement de l'essai clinique

, 1.2.8 Période d'inclusion et durée de l'étude

. .. Résultats, 1.3.1 Effet du propofol sur les ERSP pour les conditions RM et MI . . 173 7.1.3.2 Effet du propofol sur les ERSP pour les conditions MNS

M. .. Analyse,

/. .. Ers, Effet du propofol sur les modulations ERD

. Choix-de-la-fenêtre-temporelle and . .. Mns, , p.178

, Détecter l'imagination de mouvement grâce à la MNS, p.180

, 1.4.1 Modulations de l'ERD et de l'ERS en présence de propofol

. .. Conclusion-du-chapitre, , vol.186, p.7

. .. Activité-cérébrale-motrice,

. .. Conclusion-du-chapitre,

, Nous avons montré à travers nos études des chapitres 4 et 5 qu'une BCI basée sur la stimulation du nerf médian pourrait permettre de détecter l'intention de mouvement des patients victimes de réveils peropératoires, vol.464

. Cependant, En effet, il est possible que les anesthésiques aient un effet sur les modulations habituellement générées lors d'un mouvement ou d'une imagination de mouvement, rendant alors leur détection impossible [309]. Pour vérifier qu'une intention de mouvement est toujours détectable en présence d'anesthésiant

C. Dans-ce, un anesthésique intraveineux couramment utilisé) agit sur le signal EEG du cortex moteur. L'objectif principal est de vérifier que les modulations ERD et ERS sont toujours présentes lors de 4 conditions motrices (i.e. mouvement réel, imagination de mouvement, stimulation du nerf médian, mouvement imaginé pendant une stimulation du nerf médian) pour trois concentrations de propofol (0 µg.ml ?1 , 0,5 µg.ml ?1 , et 1 µg.ml ?1 ). Puis, nous évaluerons la possibilité de détecter l

, Si le chapitre 5 a montré que la stimulation du nerf médian pouvait être la clé de voûte d'une 1. Comité de Protection des Personnes Île

N. Eudract, Campus Scientifique, 615 rue du jardin botanique, 54506 Vandoeuvre-lès-Nancy 5, pp.2017-2500

. Ge-healthcare,

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S. Rimbert, M. Zaepffel, P. Riff, P. Adam, and L. Bougrain, Hypnotic state modulates sensorimotor beta rhythms during real movement and motor imagery, Frontiers in Psychology, vol.10, p.2341, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02325588

A. M. Cebolla and G. Cheron, Understanding neural oscillations in the human brain : From movement to consciousness and vice versa, Frontiers in Psychology, vol.10, p.1930, 2019.

B. Wang, Q. Bai, X. Jiao, E. Wang, and P. White, Effect of sedative and hypnotic doses of propofol on the eeg activity of patients with or without a history of seizure disorders, Journal of Neurosurgical Anesthesiology, vol.87, issue.2, pp.335-340, 1997.

H. Seifert, R. Louin, P. Conard, and J. Gross, Sedative doses of propofol increase beta activity of the processed electroencephalogram, Anesth Analg, vol.75, issue.5, pp.976-984, 1993.

F. Giovannini and L. Buhry, Tonic inhibition mediates a synchronisation enhancement during propofol anaesthesia in a network of hippocampal interneurons : a modelling study, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02447006

M. Ter-wal and P. Tiesinga, Hippocampal Oscillations, Mechanisms (PING, ING, Sparse), pp.1-14, 2013.

N. Kopell, C. Börgers, D. Pervouchine, P. Malerba, and A. Tort, Gamma and Theta Rhythms in Biophysical Models of Hippocampal Circuits, pp.423-457, 2010.

I. A. Erchova, M. A. Lebedev, and M. E. Diamond, Somatosensory cortical neuronal population activity across states of anaesthesia, European Journal of Neuroscience, vol.15, issue.4, pp.744-752, 2002.

M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, Optimizing the channel selection and classification accuracy in eeg-based bci, IEEE Transactions on Biomedical Engineering, vol.58, pp.1865-1873, 2011.

X. Yong and C. Menon, Eeg classification of different imaginary movements within the same limb, PloS one, vol.10, p.2015

H. Iwakiri, N. Nishihara, O. Nagata, T. Matsukawa, M. Ozaki et al., Individual effect-site concentrations of propofol are similar at loss of consciousness and at awakening, Anesthesia and analgesia, vol.100, pp.107-117, 2005.

F. Lotte, Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based brain-computer interfaces, Proceedings of the IEEE, vol.103, pp.871-890, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01159171

V. Jayaram, M. Alamgir, Y. Altun, B. Schölkopf, and M. Grosse-wentrup, Transfer learning in brain-computer interfaces, vol.12, p.2015

A. Clerico, A. Tiwari, R. Gupta, S. Jayaraman, and T. H. Falk, Electroencephalography amplitude modulation analysis for automated affective tagging of music video clips, Front. Comput. Neurosci, 2017.

N. Brodu, F. Lotte, and A. Lécuyer, Exploring two novel features for eeg-based braincomputer interfaces : Multifractal cumulants and predictive complexity, Neurocomputing, vol.79, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00632546

S. Rimbert, P. Guerci, D. Schmartz, C. Baumann, N. Gayraud et al., Cerebral motor activity under propofol : towards intraoperative awareness recognition using BCI. Anesthesiology, 2020.

R. Bougrain and L. , Detecting motor imagery using median nerve stimulation : a question of timing, Journal of Neural Engineering, 2020.

S. Rimbert, N. Gayraud, L. Bougrain, M. Clerc, and S. Fleck, Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor ?, Frontiers in Human Neuroscience, vol.12, p.529, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01990935

S. Rimbert, P. Riff, N. Gayraud, D. Schmartz, and L. Bougrain, Median Nerve Stimulation Based BCI : A New Approach to Detect Intraoperative Awareness During General Anesthesia, Frontiers in Neuroscience, vol.13, p.622, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02159777

S. Rimbert, D. Schmartz, and L. Bougrain, MOTANA : study protocol to investigate motor cerebral activity during a propofol sedation, Trials, vol.20, p.534, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02270649

S. Rimbert, M. Zaepffel, P. Riff, A. P. Bougrain, and L. , Hypnotic State Modulates Sensorimotor Beta Rhythms During Real Movement and Motor Imagery, Frontiers in Psychology, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02325588

S. Rimbert, R. Al-chwa, M. Zaepffel, and L. Bougrain, Electroencephalographic modulations during an open-or closed-eyes motor task, PeerJ, vol.6, p.4492, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01736600

L. Avilov, S. Rimbert, A. Popov, and L. Bougrain, submitted) Deep Learning Techniques to Improve Intraoperative Awareness Detection from Electroencephalographic Signals, IEEE EMBC, 2020.

S. Rimbert, P. Guerci, N. Gayraud, C. Meistelman, and L. Bougrain, Innovative Brain-Computer Interface based on motor cortex activity to detect accidental awareness during general anesthesia, IEEE International Conference on Systems, Man, and Cybernetics, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02166934

P. Guerci, S. Rimbert, D. Schmartz, M. R. Losser, L. Bougrain et al., Effet du propofol sur les synchronisations/désynchronisations neuronales corticales motrices analysées par une interface cerveau-machine : rapport préliminaire d'une étude prospective chez le volontaire sain, Congrès SFAR, 2019.

S. Rimbert, O. Avilov, A. P. Bougrain, and L. , Can suggestive hypnosis be used to improve Brain-Computer Interface performance, 8th Graz Brain-Computer Interface Conference, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02098864

R. Ranta, L. Cam, S. Bergmanis-korats, B. Rimbert, S. Bougrain et al., On source space resolution in EEG brain imaging for motor imagery, 9th International IEEE/EMBS Conference on Neural Engineering, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01985178

S. Rimbert, N. Gayraud, M. Clerc, S. Fleck, and L. Bougrain, Can the MIQ-RS questionnaire be used to estimate the performance of a MI-based BCI ?, Seventh International BCI Meeting, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01889864

M. Sanchez, S. Mishima, M. Fujiwara, A. G. Jouaiti, M. Kobryn et al., Methodological Design for Integration of Human EEG Data with Behavioral Analyses into Human-Human/Robot Interactions in a Real-World Context, International Conference on Innovative Computing, Information and Control, p.8, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02437374

S. Rimbert, L. Bougrain, R. Orhand, J. Nex, S. Gaborit et al., Grasp'it : une interface cerveau-ordinateur pour l'amélioration de l'apprentissage d'une tâche d'imagination motrice kinesthésique. 29ème conférence francophone sur l'Interaction Homme-Machine, 2017.

S. Rimbert, Conception d'une interface cerveau-ordinateur pour une meilleure surveillance des réveils peropératoires au cours d'une anesthésie générale. 29eme conférence francophone sur l'Interaction Homme-Machine, 2017.

C. Lindig-leon, S. Rimbert, O. Avilov, and L. Bougrain, Scalp EEG Activity During Simple and Combined Motor Imageries to Control a Robotic Arm, IEEE First Ukraine Conference on Electrical and Computer Engineering, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01519712

S. Rimbert, O. Avilov, and L. Bougrain, Discrete motor imageries can be used to allow a faster detection. 7th Graz Brain-Computer Interface Conference, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01512407

S. Rimbert, C. Lindig-león, and L. Bougrain, Profiling BCI users based on contralateral activity to improve kinesthetic motor imagery detection, 8th International IEEE EMBS Conference On Neural Engineering, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01484636

S. Rimbert, C. Lindig-león, M. Fedotenkova, and L. Bougrain, Modulation of beta power in EEG during discrete and continuous motor imageries, 8th International IEEE EMBS Conference On Neural Engineering, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01484503

S. Rimbert, S. Fleck, J. Nex, and L. Bougrain, Nécessité d'un protocole d'apprentissage progressif pour la maîtrise d'une imagination motrice après un AVC. 28ième conférence francophone sur l'Interaction Homme-Machine, pp.10-12, 2016.

S. Rimbert and . Bougrain, Comparison Between Discrete and Continuous Motor Imageries : toward a Faster Detection. International Brain Computer Interface Meeting, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01389948

S. Rimbert, L. Bougrain, C. Lindig-león, G. Serrière, F. Giovannini et al., Amplitude and latency of EEG Beta activity during real movements, discrete and continuous motor imageries, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01231404

C. Lindig-león, L. Bougrain, and S. Rimbert, Alpha rebound improves on-line detection of the end of motor imageries, IEEE EMBS Neural engineering conference, 2015.

C. Lindig-león, L. Bougrain, and S. Rimbert, On-line identification of the end of motor imageries based on the alpha rebound detection, 24th Annual Computational Neuroscience Meeting, 2015.