O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.

Les interfaces cerveau-machine (ICM) permettent de contrôler une machine directement à partir de l'activité cérébrale. Le P300-Speller, en particulier, pourrait offrir à des patients complètement paralysés, la possibilité de communiquer sans l'aide de la parole ou du geste. Nous avons cherché à améliorer cette communication en étudiant la coadaptation entre cerveau et machine. Nous avons d'abord montré que l'adaptation d'un utilisateur peut être partiellement perçue, en temps-réel, à travers les modulations de sa réponse électrophysiologique aux feedbacks de la machine. Nous avons ensuite proposé, testé et évalué les effets sur l'utilisateur de plusieurs approches permettant d'améliorer l'interaction, notamment : la correction automatique des erreurs, grâce à la reconnaissance en temps-réel des réponses aux feedbacks ; une stimulation dynamique permettant de diminuer le risque d'erreur tout en réduisant l'inconfort lié aux stimulations ; un processus automatique de décision adaptative, en fonction de l'état de vigilance du sujet. Nos résultats montrent la présence de réponses aux feedbacks spécifiques des erreurs et modulées par l'attention ainsi que par la surprise du sujet face au résultat de l'interaction. Par ailleurs, si l'efficacité de la correction automatique est variable d'un sujet à l'autre, le nouveau mode de stimulation comme la décision adaptative apparaissent comme très avantageux et leur utilisation a un effet positif sur la motivation. Dans la perspective d'études cliniques pour évaluer l'utilité des ICM pour la communication, ces travaux soulignent et quantifient l'intérêt de développer des interfaces capables de s'adapter à chaque utilisateur / Brain-computer interfaces (BCI) aim at enabling the brain to directly control an artificial device. In particular, the P300-Speller could offer patients who cannot speak and neither move, to communicate again. This work consisted in improving this communication by implementing and studying a coadaptation between the brain and the machine. First, on the user side, we showed that adaptation is reflected in real-time by modulations of the electrophysiological responses to the feedbacks from the machine. Then, on the computer side, we proposed, tested and evaluated the effect on the user, of several approaches that endow the machine with adaptive behavior, namely: Automatic correction of errors, based on real-time recognition of feedback responses; Dynamic stimulation to increase spelling accuracy as well as to reduce the discomfort associated with the traditional row/column stimulation paradigm; Adaptive decision making for optimal stopping, depending on the attentional state of the user. Our results show the presence of feedback responses which are error specific and modulated by attention as well as user's surprise with respect to the outcome of the interaction. Besides, while the interest of automatic correction is highly subject-dependant, the new stimulation mode and the adaptive decision method proved clearly beneficial and their use had a significant positive impact on subject's motivation. In the perspective of clinical studies to assess the usefulness of ICM for communication, this work highlights and quantifies the importance of developing adaptive interfaces that are tailored to each every individual

A Brain-Computer Interface (BCI) is a system to communicate with external world through the brain activity. The brain activity is measured by Electro-Encephalography (EEG) and then processed by a BCI system. EEG source reconstruction could be a way to improve the accuracy of EEG classification in EEGbased brain–computer interface (BCI). In this thesis BCI methods were applied on derived sources which by their EEG enhancement it became possible to obtain a more accurate EEG detection and brought a new application to BCI technology that are recognition of writing letters imagery from brain waves. The BCI system enables people to write and type letters by their brain activity (EEG). To this end, first part of the thesis is dedicated to EEG source reconstruction techniques to select the most optimal EEG channels for task classification purposes. Due to this reason the changes in EEG signal power from rest state to motor imagery task was used, to find the location of an active single equivalent dipole. Implementing an inverse problem solution on the power changes by Multiple Sparse Priors (MSP) method generated a scalp map where its fitting showed the localization of EEG electrodes. Having the optimized locations the secondary objective was to choose the most optimal EEG features and rhythm for an efficient classification. This became possible by feature ranking, 1- Nearest Neighbor leave-one-out. The feature vectors were computed by applying the combined methods of multitaper method, Pwelch. The features were classified by several methods of Normal densities based quadratic classifier (qdc), k-nearest neighbor classifier (knn), Mixture of Gaussians classification and Train neural network classifier using back-propagation. Results show that the selected features and classifiers are able to recognize the imagination of writing alphabet with the high accuracy. / BCI controls external devices and interacts with the environment by brain signals. Measured EEG signals over the motor cortex exhibit changes in power related to the movements or imaginations which are executed in motor tasks [1]. These changes declare increase or decrease of power in the alpha (8Hz-13Hz), and beta (13Hz-28Hz) frequency bands from resting state to motor imagery task that known as event related synchronization (in case of power increasing) and desynchronization (in case of power decreasing) [2]. The necessity to communicate with the external world for locked-in state (LIS) patients (a paralyzed patient who only communicates with eyes), made doctors and engineers motivated to develop a BCI technology for typing letters through brain commands. Many researches have been done around this area to ascertain the dream of typing for handicapped. In the brain some regions of the cerebral cortex (motor cortex) are involved in the planning, control, and execution of voluntary movements. Electroencephalography (EEG) signals are electrical potential generated by the nerve cells in the cerebral cortex. In order to execute motoric tasks, the EEG signals are appeared over the motor cortex [1]. The measured brain response to a stimulus is called eventrelated potential (ERP). P300-event related potential (ERP) is an evoked neuron response to an external auditory or visual stimulus that is detectable in scalp-recorded EEG (The P300 is evoked potential which occurs across the parieto-central on the skull 300 ms after applying the stimulus). Farwell and Donchin have proven in a P300-based BCI speller [3] that P300 response is a reliable signal for controlling a BCI system. They described the P300 speller, in which alphanumeric characters are represented in a matrix grid of six-by-six matrix. The user should focus on one of the 36 character cells while each row and column of the grid is intensified randomly and sequentially. The P300, observed in EEG signals, is created by the intersection of the target row and column which causes detection of the target stimuli with a probability of 1/6 (in case of high accuracy of flashing operation). Also when the target stimulus is rarely presented in the random sequence of stimuli causes a neural reaction to unpredictable but recognizable event and a P300 response is evoked [3]. Generally when the subject is involved with the task to recognize the targets, the P300 wave happens and the signal amplitude varies with the unlikelihood of the targets. Its dormancy changes with the difficulty of recognizing the target stimulus from the standard stimuli [3].The attended character of the matrix can be extracted by proper feature extraction and classification of P300. A plenty of procedures for feature extraction and classification have been applied to improve the performance of originally reported speller [3], such as stepwise linear discriminate analysis (SWLDA) [4, 5], wavelets [1], support vector machines [6, 7, 8] and matched filtering [9]. Till now, BCI-related P300 research has mostly considered on signals from standard P300 scalp locations. While in [10, 11, 12, 13, 14, 15, 16] it has been proven that the use of additional locations, especially posterior sites, may improve classification accuracy, but it has not been addressed to particular offline and online studies. Recently, auditory version improvement of the visual P300 speller allows locked in patients who have problem in the visual system to use the P300 speller system by relating two numbers to each letter which indicate the row and column of letter position [17]. Now a new technology is needed which can substitute a keyboard with no alphabet menu. The technology will be handy for blind people and useful for healthy persons who need to work hands free with their computer or mobile. The aim of this thesis is to improve EEG detection through source localization for a new BCI application to type with EEG signals without using alphabet menu. / +98-9359576229

Introduction. Les animaux et les humains sont capables de moduler leur propre activité cérébrale, pourvu que leur soit donné un retour sensoriel en temps-réel de celle-ci. La gamme des activités contrôlables s’étend des rythmes oscillatoires, à la réponse hémodynamique , au taux de décharge des neurones ou même au signal calcique associé aux potentiels d’action. Le contrôle volontaire des activités neuronales, facilité par le plan expérimental d’un paradigme en boucle fermée, est au cœur de l’interaction corps-esprit et peut être utilisé pour adresser des questions philosophiques. Mais comme de nombreuses études l’ont démontré, les interfaces homme-machine sont aussi un outil puissant dans la réhabilitation motrice, la gestion de la douleur, la régulation des émotions, ou encore l’amélioration de la mémoire. Étant donné que la plupart des études a été conduite sur les sujets humains avec des techniques non-invasives, les mécanismes neurophysiologiques de l’autorégulation neuronale sont restés mal connus. L’objectif principal de ce travail était donc d’élaborer une description des principes physiologiques sous-tendant cette technique.Objectifs. D’après la théorie des oscillations neuronales à des multiples niveaux, la présente enquête était principalement définie par les questions suivantes : 1) Quels sont les marqueurs physiologiques du contrôle volontaire des activités neuronales? 2) Existe t-il des échelles spatiotemporelle plus facilement modulables que d’autres? 3) Les effets de l’entrainement sont –ils spécifiques ou généralisables en espace et fréquence ? et 4) Quelles sont les stratégies cognitives efficace pour contrôler les activités oscillatoires parmi plusieurs sujets ? Pour adresser ces questions, dans mon travail j’ai utilisé les enregistrements intracérébraux avec des macro- et micro-électrodes chez les patients épileptiques dans le cadre d’un bilan pré-chirurgical. / Introduction. Animals and humans are capable to modulate their own brain activity if they are provided with real-time sensory feedback thereof. The range of controllable neural activities reaches from oscillatory brain rhythms, over hemodynamic response function to the firing of single neurons or even action-potential associated calcium signals. The voluntary control of neural activity facilitated by this ‘closed-loop’ experimental paradigm is at the very heart of the mind-body interaction and can be used to address philosophical questions. But as numerous successful applications of neurofeedback and brain-computer interfaces have demonstrated, it is also a powerful tool in motor rehabilitation, pain management, emotion regulation or memory improvement. Because most previous studies were conducted on humans using non-invasive recordings techniques, the neurophysiological mechanisms of neural self-regulation remained obscure. The main objective of the present work was thus to provide a better understanding of its underlying principles. Objectives. Following a multiscale theoretical framework of neural oscillations, the present investigation was largely guided by the following questions: 1) What are the physiological markers of successful control? 2) Are some regions or spatiotemporal scales more easily controllable than others? 3) Are training effects specific or generalized? and 4) What are subject-invariant successful cognitive strategies of neural self-control? To address these questions, we took advantage of intracerebral macro- and micro-electrode recordings in epileptic patients undergoing long-term monitoring in the presurgical context.

Les Interfaces Cerveau-Machine (ICM) ont été appliquées avec succès aussi bien dans le domaine clinique que pour l'amélioration de la vie quotidienne de patients avec des handicaps. En tant que composante essentielle, le module de traitement du signal détermine nettement la performance d'un système ICM. Nous nous consacrons à améliorer les stratégies de traitement du signal du point de vue de l'apprentissage de la machine. Tout d'abord, nous avons développé un algorithme basé sur les SVM transductifs couplés aux noyaux multiples afin d'intégrer différentes vues des données (vue statistique ou vue géométrique) dans le processus d'apprentissage. Deuxièmement, nous avons proposé une version enligne de l'apprentissage multi-noyaux dans le cas supervisé. Les résultats expérimentaux montrent de meilleures performances par rapport aux approches classiques. De plus, l'algorithme proposé permet de sélectionner automatiquement les canaux de signaux EEG utiles grâce à l'apprentissage multi-noyaux.Dans la dernière partie, nous nous sommes attaqués à l'amélioration du module de traitement du signal au-delà des algorithmes d'apprentissage automatique eux-mêmes. En analysant les données ICM hors-ligne, nous avons d'abord confirmé qu'un modèle de classification simple peut également obtenir des performances satisfaisantes en effectuant une sélection de caractéristiques (et/ou de canaux). Nous avons ensuite conçu un système émotionnel ICM par en tenant compte de l'état émotionnel de l'utilisateur. Sur la base des données de l'EEG obtenus avec différents états émotionnels, c'est-à -dire, positives, négatives et neutres émotions, nous avons finalement prouvé que l'émotion affectait les performances ICM en utilisant des tests statistiques. Cette partie de la thèse propose des bases pour réaliser des ICM plus adaptées aux utilisateurs. / Brain-computer Interface (BCI) has achieved numerous successful applications in both clinicaldomain and daily life amelioration. As an essential component, signal processing determines markedly the performance of a BCI system. In this thesis, we dedicate to improve the signal processing strategy from perspective of machine learning strategy. Firstly, we proposed TSVM-MKL to explore the inputs from multiple views, namely, from statistical view and geometrical view; Secondly, we proposed an online MKL to reduce the computational burden involved in most MKL algorithm. The proposed algorithms achieve a better classifcation performance compared with the classical signal kernel machines, and realize an automatical channel selection due to the advantages of MKL algorithm. In the last part, we attempt to improve the signal processing beyond the machine learning algorithms themselves. We first confirmed that simple classifier model can also achieve satisfying performance by careful feature (and/or channel) selection in off-line BCI data analysis. We then implement another approach to improve the BCI signal processing by taking account for the user's emotional state during the signal acquisition procedure. Based on the reliable EEG data obtained from different emotional states, namely, positive, negative and neutral emotions, we perform strict evaluation using statistical tests to confirm that the emotion does affect BCI performance. This part of work provides important basis for realizing user-friendly BCIs.

Malgré des développements récents dans la conception des casques électroencéphalographiques (EEG) et dans l'analyse du signal cérébrale, les interfaces cerveau-machine (ICM) sont toujours restreintes au contexte de la recherche scientifique. Toutefois, les ICM gagneraient à intégrer la réalité virtuelle (VR) car elles diminuent la distance entre l'utilisateur et son avatar en remplaçant les commandes mécaniques, et donc améliorent le sentiment d'immersion. De plus, les ICM fournissent également des informations sur l'état mental de l'utilisateur comme sa concentration, son attention ou ses points d'intérêts dans l'environnement virtuel. Dans cette thèse, nous étudions l'interaction entre ICM et VR, qui constituent une interface homme-machine dont les applications nous semblent prometteuses pour le grand public, particulièrement dans le domaine du divertissement. Nous nous concentrons sur l'utilisation d’une ICM basée sur l'EEG et sur une stimulation visuelle occasionnelle, autrement-dit une ICM basée sur la détection du P300, un potentiel évoqué positif apparaissant dans l’EEG 250 à 600 ms après l’apparition d’un stimulus. Nous étudions l’usage de cette ICM pour interagir avec un environnement simulé grâce à un casque de VR mobile utilisant un smartphone ordinaire, c’est-à-dire un matériel de VR approprié pour une utilisation grand public.Toutefois, l’intégration des ICM dans la VR pour le grand public rencontre aujourd’hui des défis d’ordre technique, expérimental et conceptuel. En effet, l’utilisation d’un matériel mobile pose des contraintes techniques considérables, et une telle technologie n’a pas encore fait l’objet d’une validation. Les facteurs influençant la performance des ICM en VR par rapport à une utilisation sur PC restent flous. Également, les caractéristiques physiologiques de la réponse du cerveau à des stimuli en VR par rapport aux mêmes stimuli présentés sous PC sont inconnues. Finalement, les ICM et la VR admettent des limites considérables, parfois incompatibles. Par exemple, l’utilisateur interagit avec la VR en bougeant, ce qui perturbe le signal EEG et diminue la performance de l’ICM. Il y a donc une nécessité d’adapter le design de l’application pour une technologie mixte ICM+VR.Dans ce travail, nous présentons une contribution dans chacun de ces domaines : une réalisation technique vers une ICM grand public en VR ; une analyse expérimentale de sa performance et une analyse des différences physiologies produites par des stimuli présentés dans un casque de VR par rapport aux mêmes stimuli sous PC - analyses menées à bien grâce à deux campagnes expérimentales portant sur 33 sujets ; une synthèse des recommandations pour un design adapté à la fois aux ICM et à la VR. / In spite of ongoing developments in the conception of electroencephalographic (EEG) headsets and brain signal analysis, the actual use of an EEG-based brain-computer interfaces (BCI) is still restricted to research settings. On the other hand, BCI technology candidates as a good complement to virtual reality as it may diminish the distance between the user and his/her avatar. A BCI can accomplish this by circumventing the usual muscular pathway between the brain and the machine, thus enhancing the immersion feeling in VR applications. Moreover, a BCI provides valuable information on the mental state of the user, such as concentration and attention for the task at hand and for the virtual objects of interest. In this thesis, we study the coupling of BCI and VR technology, a human-machine interface that is potentially ubiquitous, in particular for gaming. We focus on the use of EEG-based BCI with occasional visual stimulation, i.e., BCIs based on the detection of the P300, a positive evoked potential appearing in the EEG 250 to 600 ms after the presentation of a stimulus. We investigate the use of such a BCI to interact with VR environments obtained using a mobile head-mounted display based on an ordinary smartphone, material suiting well the general public.The fusion of BCI technology with VR faces technical, experimental and conceptual limitations. Indeed, the integration of BCI with a mobile head-mounted display is technically burdensome and has not been fully validated. The factors impacting the performance of the BCI in VR remains still unknown. Also, unknown are the physiological characteristics of the brain responses to VR stimuli as compared to the same stimuli displayed on a PC screen. Finally, both BCI and VR technologies are limited, and these limitations sometimes appears contradictory. For example, the EEG is perturbed by the user’s movements while s/he is interacting with the virtual environment, but this movement may be an essential aspect of the VR experience. Thus, it is necessary to operate a synthesis of the existing design recommendations for BCI and VR technologies from the perspective of a mixed BCI+VR application.In this work, we presents three contributions: a technical implementation of a BCI+VR system, paving the way for a general public use; an analysis of its performance and an analysis of the physiological differences produced by VR stimuli as compared to the same stimuli on a PC by means of two experimental campaigns carried out on 33 subjects; a synthesis of the recommendations to adapt the application design to BCI and VR.

Modern Technologies in Medicine and Law Abstract This thesis deals with the application of modern technologies in medicine from the perspective of law. The primary aim of this work is to analyse the given provisions of the Civil Code, Act on Health Services and Act on Medical Devices, and also to determine whether the current legislation represents a suitable legal framework able to respond to the implementation of modern technology in the healthcare sector. In connection with this analysis, author presents possibilities of legislative changes that would respond to these modern technologies. The work inter alia deals with the question of whether the use of some of these technologies within the provision of healthcare services can be considered compliant with the principle of lege artis. In the first chapter, the reader is introduced to the topic of the thesis. This chapter defines the basic terms and presents an overview of the legislation related to the chosen topic. The second chapter represents a main part of the thesis, where author deals with the topic of artificial intelligence. In this chapter, the reader is acquainted with the term of artificial intelligence and the definition of its legal status. Subsequently, author evaluates whether the current legislation constitutes appropriate legal frameworks...

Hjärndatorgränssnitt är enheter som fångar upp hjärnsignaler via elektroder på huvudet och översätter dem till datamängder och instruktioner mot externa enheter och applikationer. Gränssnitten har främst använts inom den medicinska domänen för att hjälpa personer med neurofysiologiska åkommor, men har även på senare tid börjat användas av ickemedicinska skäl av privatpersoner. I takt med att gränssnitten ökar i popularitet och når en bredare massa kommer det att innebära ett större informationsflöde av användardata som i sin tur kan bära på väldigt känslig information. Information såsom hälsodata och autentiseringsmetoder är några av flera informationstillgångar som ligger i farozonen enligt flera artiklar och kan råka ut för ett eller flera hot. För få en tydligare bild av de olika hoten samt dess konsekvens och sannolikhet har det genomförts en riskanalys gällande hemanvändares informationssäkerhet. För att få fram sårbarheter, hot och åtgärder som förekommer i riskanalysen har det utförts en tematisk analys. Genom den tematiska analysen visade det sig att det fanns flera hot mot hemanvändarnas konfidentialitet där användares PIN-koder, autentiseringsmetoder och hälsodata låg i farozonen. För att få en bättre förståelse kring hur gränssnitten fungerar samt hur stor sannolikhet det är för olika hot har det även genomförts en intervju med en lektor i kognitiv neurovetenskap, följande tillsammans med artiklarna från den tematiska analysen utgjorde därmed grunden för riskanalysen. Genom riskanalysen visade det sig att hoten mot hemanvändarnas möjlighet att använda gränsssnitten hade en ännu större sannolikhet att inträffa än hot mot användares konfidentialitet. / Brain- Computer Interfaces are devices that capture brain signals via electrodes on the head and then translates them into data sets and instructions to external devices and applications. The interfaces have mainly been used in the medical domain to help people with neurophysiological disorders but have also recently begun to be used for non-medical reasons by private persons. As the interfaces increase in popularity and reach a wider mass, it will mean a greater flow of information of user data that in turn can carry very sensitive information. Information such as health data and authentication methods are some of several information assets that are at risk according to multiple articles and may face one or more threats. To get a clearer picture of the various threats, their consequences and probabilities, a risk analysis has been carried out. In order to identify vulnerabilities, threats and measures that appear in the risk analysis, a thematic analysis has been performed. The thematic coding showed that there were several threats to the home user’s confidentiality where user’s PIN-codes and health data were at risk. In order to gain a better understanding of how the interfaces work and how likely it is for various threats to succeed, an interview was conducted with a senior lectrurer in cognitive neuroscience, the following together with the articles from the thematic analysis thus formed the basis for the risk analysis. The risk analysis showed that threats to home users' ability to use the interfaces were even more likely to occur than threats to user confidentiality.

Objective: Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities. Methods: Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6. ×. 6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects. Results: BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN). Conclusions: The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate). Significance: P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.

Le vaste domaine de l’amélioration cognitive traverse les applications comportementales, biochimiques et physiques. Aussi nombreuses sont les techniques que les limites de ces premières : des études de pauvre méthodologie, des pratiques éthiquement ambiguës, de faibles effets positifs, des effets secondaires significatifs, des couts financiers importants, un investissement de temps significatif, une accessibilité inégale, et encore un manque de transfert. L’objectif de cette thèse est de proposer une méthode novatrice d’intégration de l’une de ces techniques, le neurofeedback, directement dans un paradigme d’apprentissage afin d’améliorer la performance cognitive et l’apprentissage. Cette thèse propose les modalités, les fondements empiriques et des données à l’appui de ce paradigme efficace d’apprentissage ‘bouclé’. En manipulant la difficulté dans une tâche en fonction de l’activité cérébrale en temps réel, il est démontré que dans un paradigme d’apprentissage traditionnel (3-dimentional multiple object tracking), la vitesse et le degré d’apprentissage peuvent être améliorés de manière significative lorsque comparés au paradigme traditionnel ou encore à un groupe de contrôle actif. La performance améliorée demeure observée même avec un retrait du signal de rétroaction, ce qui suggère que les effets de l’entrainement amélioré sont consolidés et ne dépendent pas d’une rétroaction continue. Ensuite, cette thèse révèle comment de tels effets se produisent, en examinant les corrélés neuronaux des états de préparation et de performance à travers les conditions d’état de base et pendant la tâche, de plus qu’en fonction du résultat (réussite/échec) et de la difficulté (basse/moyenne/haute vitesse). La préparation, la performance et la charge cognitive sont mesurées via des liens robustement établis dans un contexte d’activité cérébrale fonctionnelle mesurée par l’électroencéphalographie quantitative. Il est démontré que l’ajout d’une assistance- à-la-tâche apportée par la fréquence alpha dominante est non seulement appropriée aux conditions de ce paradigme, mais influence la charge cognitive afin de favoriser un maintien du sujet dans sa zone de développement proximale, ce qui facilite l’apprentissage et améliore la performance. Ce type de paradigme d’apprentissage peut contribuer à surmonter, au minimum, un des limites fondamentales du neurofeedback et des autres techniques d’amélioration cognitive : le manque de transfert, en utilisant une méthode pouvant être intégrée directement dans le contexte dans lequel l’amélioration de la performance est souhaitée. / The domain of cognitive enhancement is vast, spanning behavioral, biochemical and physical applications. The techniques are as numerous as are the limitations: poorly conducted studies, ethically ambiguous practices, limited positive effects, significant side-effects, high financial costs, significant time investment, unequal accessibility, and lack of transfer. The purpose of this thesis is to propose a novel way of integrating one of these techniques, neurofeedback, directly into a learning context in order to enhance cognitive performance and learning. This thesis provides the framework, empirical foundations, and supporting evidence for a highly efficient ‘closed-loop’ learning paradigm. By manipulating task difficulty based on a measure of cognitive load within a classic learning scenario (3-dimentional multiple object tracking) using real-time brain activity, results demonstrate that over 10 sessions, speed and degree of learning can be substantially improved compared with a classic learning system or an active sham-control group. Superior performance persists even once the feedback signal is removed, which suggests that the effects of enhanced training are consolidated and do not rely on continued feedback. Next, this thesis examines how these effects occur, exploring the neural correlates of the states of preparedness and performance across baseline and task conditions, further examining correlates related to trial results (correct/incorrect) and task difficulty (slow/medium/fast speeds). Cognitive preparedness, performance and load are measured using well-established relationships between real-time quantified brain activity as measured by quantitative electroencephalography. It is shown that the addition of neurofeedback-based task assistance based on peak alpha frequency is appropriate to task conditions and manages to influence cognitive load, keeping the subject in the zone of proximal development more often, facilitating learning and improving performance. This type of learning paradigm could contribute to overcoming at least one of the fundamental limitations of neurofeedback and other cognitive enhancement techniques : a lack of observable transfer effects, by utilizing a method that can be directly integrated into the context in which improved performance is sought.