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Brain-computer interfaces (BCIs) are very convenient tools to assess locked-in (LIS) and completely locked-in state (CLIS) patients' hidden states of consciousness. For the time being, there is no ground-truth data in respect to these states for above-mentioned patients. This lack of gold standard makes this problem particularly challenging. In addition to consciousness assessment, BCIs also provide them with a communication device that does not require the presence of motor responses, which they are lacking. Communication plays an important role in the patients' quality of life and prognosis. Significant progress have been made to provide them with EEG-based BCIs in particular. Nonetheless, the majority of existing studies directly dive into the communication part without assessing if the patient is even conscious. Additionally, the few studies that do essentially use evoked brain potentials, mostly the P300, that necessitates the patient's voluntary and active participation to be elicited. Patients are easily fatigued, and would consequently be less successful during the main communication task. Furthermore, when the consciousness states are determined using resting state data, only one or two features were used. In this thesis, different sets of EEG features are used to assess the consciousness level of CLIS patients using resting-state data. This is done as a preliminary step that needed to be succeeded in order to engage to the next step, communication with the patient. In other words, the 'conversation' is initiated only if the patient is sufficiently conscious. This variety of EEG features is utilised to increase the probability of correctly estimating the patients' consciousness states. Indeed, each of them captures a particular signal attribute, and combining them would allow the collection of different hidden characteristics that could have not been obtained from a single feature. Furthermore, the proposed method should allow to determine if communication shall be initiated at a specific time with the patient. The EEG features used are frequency-based, complexity related and connectivity metrics. Besides, instead of analysing results from individual channels or specific brain regions, the global activity of the brain is assessed. The estimated consciousness levels are then obtained by applying two different soft-clustering analysis methods, namely Fuzzy c-means (FCM) and Gaussian Mixture Models (GMM), to the individual features and ensembling their results using their average or their product. The proposed approach is first applied to EEG data recorded from patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) (patients with disorders of consciousness (DoC)) to evaluate its performance. It is subsequently applied to data from one CLIS patient that is unique in its kind because it contains a time frame during which the experimenters affirmed that he was conscious. Finally, it is used to estimate the levels of consciousness of nine other CLIS patients. The obtained results revealed that the presented approach was able to take into account the variations of the different features and deduce a unique output taking into consideration the individual features contributions. Some of them performed better than others, which is not surprising since each person is different. It was also able to draw very accurate estimations of the level of consciousness under specific conditions. The approach presented in this thesis provides an additional tool for diagnosis to the medical staff. Furthermore, when implemented online, it would enable to determine the optimal time to engage in communication with CLIS patients. Moreover, it could possibly be used to predict patients' cognitive decline and/or death.

Interface cerveau-machine pour compenser les déficits moteurs chez des patients ayant des troubles moteurs, avec des implantations chroniques d'électrodes corticales. Etude expérimentale sur animaux. Une interface cerveau-machine (ICM) est définie comme un système de communication qui permet à l'activité cérébrale seule de contrôler des effecteurs externes. L'objectif immédiat des ICM est de fournir des capacités de communication aux personnes gravement handicapées qui sont totalement paralysées par des troubles neuromusculaires, tels que la sclérose latérale amyotrophique, l'accident vasculaire cérébral ou une lésion de la moelle épinière. Des résultats prometteurs (des patients pilotent un joystick grâce à la modulation de leur activité corticale) permettre d'accroître l'espoir dans de futures applications d'ICM avec une matrice de microélectrodes implantées chroniquement à la surface du cortex. Des expériences récentes ont démontré la capacité d'un tétraplégique à contrôler un bras robotisé. Ce travail de thèse contribue aux études précliniques, réalisées en parallèle du développement technique afin de fournir la validation du protocole expérimental chez l'homme par étapes successives. Il permet de développer un dispositif d'enregistrement ElectroCorticoGramme (ECoG) chez des rats, pour l'implanter chez ces animaux et enregistrer leur activité ECoG lors d'expériences comportementales pour contrôler un effecteur externe. Deux types d'études en ligne ont été effectués: le contrôle du distributeur directement par l'activité corticale ou par la combinaison de la tâche motrice (appuyer sur la pédale) et la détection de la signature. Dans les études de contrôle direct par la détection, la Performance Générale (PG) de notre ICM a été de 21,01% ± 4,33 (10 animaux 69 expériences), mais le nombre d'appuis par minute est tombé à 0,57±0,47 rendant plus difficile l'interprétation de ces résultats. C'est pourquoi les expériences, plus complexes, nécessitant l'activation du levier et la détection de signature ont été réalisés. La PG, dans ce cas, est de 37,76% ± 9,64 avec un nombre d'appuis qui a augmenté à 3,24 ± 0,7. La comparaison avec une détection aléatoire nous a permis d'être sûr que ces résultats ne sont pas aléatoires (environ 25-30 fois plus que l'analyse aléatoire). L'une des caractéristiques la plus intéressante de ces expériences est que la zone qui semble en évidence concernée par l'exécution de la tâche motrice est la région du cervelet et non la zone motrice et sensori-motrice, zones qui étaient attendues, comme pour les humains. Un aspect de notre étude sur la neuroplasticité a été de démontrer que la signature, une fois identifiée sur le cervelet, peut être détectée en temps réel dans d'autres régions du cerveau. Nos résultats ont montré une PG de 15,16% ± 3,75 dans 97 expériences faites sur 8 rats. Ces résultats ont montré que l'activité cérébrale en corrélation avec la tâche comportementale, identifiée en premier lieu dans le cervelet, peut être détectée dans une zone différente du cerveau. La caractéristique principale de ce travail de thèse est la démonstration que l'activité neuronale enregistrée en continu au niveau d'une électrode corticale unique peut être efficacement utilisée pour piloter un effecteur avec un degré de liberté, au cours d'expériences longue durant jusqu'à une heure, avec un animal libre de ses mouvements capable de prendre des décisions de manière aléatoire sans indication. Ce travail est une étape déterminante, un premier pas, vers un programme plus vaste visant à fournir un certain niveau de mobilité pour des jeunes patients tétraplégiques.

This thesis presents methods for detecting consciousness in patients with complete locked-in syndrome (CLIS). CLIS patients are unable to speak and have lost all muscle movement. Externally, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to be still conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is vital to develop alternative ways to re-establish communication with these patients during periods of awareness, and a possible platform is through brain–computer interface (BCI). Since consciousness is required to use BCI correctly, this study proposes a modus operandi to analyze not only in intracranial electrocorticography (ECoG) signals with greater signal-to-noise ratio (SNR) and higher signal amplitude, but also in non-invasive electroencephalography (EEG) signals. By applying three different time-domain analysis approaches sample entropy, permutation entropy, and Poincaré plot as feature extraction to prevent disease-related reductions of brainwave frequency bands in CLIS patients, and cross-validated to improve the probability of correctly detecting the conscious states of CLIS patients. Due to the lack a of 'ground truth' that could be used as teaching input to correct the outcomes, k-Means and DBSCAN these unsupervised learning methods were used to reveal the presence of different levels of consciousness for individual participation in the experiment first in locked-in state (LIS) patients with ALSFRS-R score of 0. The results of these different methods converge on the specific periods of consciousness of CLIS/LIS patients, coinciding with the period during which CLIS/LIS patients recorded communication with an experimenter. To determine methodological feasibility, the methods were also applied to patients with disorders of consciousness (DOC). The results indicate that the use of sample entropy might be helpful to detect awareness not only in CLIS/LIS patients but also in minimally conscious state (MCS)/unresponsive wakefulness syndrome (UWS) patients, and showed good resolution for both ECoG signals up to 24 hours a day and EEG signals focused on one or two hours at the time of the experiment. This thesis focus on consistent results across multiple channels to avoid compensatory effects of brain injury. Unlike most techniques designed to help clinicians diagnose and understand patients' long-term disease progression or distinguish between different disease types on the clinical scales of consciousness. The aim of this investigation is to develop a reliable brain-computer interface-based communication aid eventually to provide family members with a method for short-term communication with CLIS patients in daily life, and at the same time, this will keep patients' brains active to increase patients' willingness to live and improve their quality of life (QOL).

Completely locked-in state (CLIS) patients are unable to speak and have lost all muscle movement. From the external view, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to still be conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is important to find alternative ways to re-establish communication with these patients during periods of awareness, and one such alternative is through a brain–computer interface (BCI). In this study, multiscale-based methods (multiscale sample entropy, multiscale permutation entropy and multiscale Poincaré plots) were applied to analyze electrocorticogram signals from a CLIS patient to detect the underlying consciousness level. Results from these different methods converge to a specific period of awareness of the CLIS patient in question, coinciding with the period during which the CLIS patient is recorded to have communicated with an experimenter. The aim of the investigation is to propose a methodology that could be used to create reliable communication with CLIS patients.

Epilepsy, as the most common chronic neurological disease, affects a significant part of population (0.5-1%). Drug resistant epilepsy has a significant negative effect on the quality of life, psychiatric comorbidities, neurocognitive performance and the risk of SUDEP in children. Therefore, resective epilepsy surgery, the only curative treatment of this condition, can fundamentally reverse this unfavorable prognosis. An inevitable prerequisite for a good postoperative result is complete removal of the epileptogenic zone (EC) and preservation of eloquent areas (EC). At present, even with improving and new preoperative non-invasive methods, we don't have an exclusive diagnostic method for theirs delineation. The aim of this PhD study is to assess benefit of individual intraoperative electrophysiological (iEF) methods in pediatric patients with focal intractable epilepsy. The first study evaluates the importance of intraoperative electrocorticography (iECoG) in the localization of EZ. The study proved that iECoG serves as a reliable tool to guide surgical resection and may predict results of epilepsy surgery. iECoG-based modification of surgical plan is not associated with increased risk of significant complications. The second presented study analyzed the contribution of intraoperative electrical...