Since the introduction of the P300 BCI speller by Farwell and Donchin1 speed and accuracy of the system has been significantly improved. Larger electrode montages and various signal processing techniques are responsible for most of the improvement in performance. The present study takes advantage of a new presentation paradigm to improve performance, the “checkerboard?(CB) paradigm2. The CB presents quasi-random groups of six items instead of using the typical row/column presentation. To determine if reducing distraction from neighbouring items could improve subsequent performance on a copy-spelling task, the CB paradigm was used and compared to a condition that suppressed (i.e., did not flash) items during the calibration phase of the experiment.

Since its inception, the P300-based BCI has typically flashed in rows and columns [1]. Recently, the “checkerboard?(CB) paradigm was introduced in which targets are grouped in rows and columns on two “virtual matrices?taken from the white and black squares of a checkerboard overlaid on the physical matrix [2]. Disassociating the physical rows and columns of the matrix from how they are grouped to flash brings advantages by: 1) avoiding the problematic effects of double target flashes [3], and 2) not allowing adjacent targets to flash together. In this study, this disassociation of the “flash groups?from the physical matrix is taken further. The flash groups become purely “abstract?bearing no relationship to rows or columns either physical or virtual. This study compares performance of this new paradigm named ?-Flash?(5F) to the CB.

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Context: Motor Imagery (MI) signal classification is a crucial task for developing Brain-Computer Interfaces (BCIs) that allow people to control devices using their thoughts. However, traditional machine learning approaches often suffer from limited performance due to inter-subject variability and limited data availability. In response, adversarial learning has emerged as a promising solution to enhance the resilience and accuracy of BCI systems. However, to the best of our knowledge, there has not been a review of the literature on adversarial learning specifically focusing on MI classification. Objective: The objective of this thesis is to perform a Systematic Literature Review (SLR) focusing on the latest techniques of adversarial learning used to classify motor imagery signals. It aims to analyze the publication trends of the reviewed studies, investigate their use-cases, and identify the challenges in the field. Additionally, this research recognizes the datasets used in previous studies and their associated use-cases. It also identifies the pre-processing and adversarial learning techniques, and compare their performance. Additionally, it could aid in evaluating the replicability of the studies included. The outcomes of this study will assist future researchers in selecting appropriate datasets, pre-processing, and adversarial learning techniques to advance their research objectives. The comparison of models will also provide practical insights, enabling researchers to make informed decisions when designing models for motor imagery classification. Furthermore, assessing reproducibility might help in validating the research outcomes and hence elevate the overall quality of future research. Method: A thorough and systematic search following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines is undertaken to gather primary research articles from several databases such as Scopus, Web of Science, IEEEXplore, PubMed, and ScienceDirect. Two independent reviewers evaluated the articles obtained based on predetermined eligibility criteria at the title-abstract level, and their agreement was measured using Cohen's Kappa. The articles that fulfill the criteria are then scrutinized at the full-text level by the same reviewers. Any discrepancies are resolved by the judge – played by the supervisor. Critical appraisal was employed to choose appropriate studies for data extraction, which was subsequently examined using bibliometric and descriptive analyses to answer the research questions. Result: The study's findings indicate substantial growth within the domain over the past six years, notably propelled by contributions from the Asian region. However, the need for augmented collaboration becomes evident as evidenced by the prevalence of insular co-author networks. Four principal use-cases for adversarial learning are identified, spanning data augmentation, domain adaptation, feature extraction, and artifact removal. The favored datasets are BCI Competition IV's 2a and 2b, often accompanied by band-pass filtering and exponential moving standardization preprocessing. This study identifies two primary adversarial learning techniques: GAN and Adversarial Training. GAN is mainly used for data augmentation and artifact removal, while adversarial training is employed for domain adaptation and feature extraction. Based on the results reported in the chosen papers, the accuracy achieved for data augmentation and domain adaptation use cases is nearly identical at 95.3%, while the highest accuracy for the feature extraction use case is 86.91%. However, for artifact removal, both correlation and root mean square methods have been referenced. Furthermore, a reproducibility table has been established which may help in evaluating the replicability of the selected studies . Conclusion: The outcomes provide researchers with valuable perspectives on less-explored areas that hold room for additional enhancement. Ultimately, these perspectives hold the promise of improving the practical applications intended to support individuals dealing with motor impairments.

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L'objectif de ces travaux de recherche est l'étude et le développement d'un système ICM embarqué en utilisant la méthodologie de conception conjointe afin de satisfaire ses contraintes spécifiques. Il en a découlé la constitution d'un système ICM complet intégrant un système d'acquisition OpenBCI et un système de traitement à base de FPGA. Ce système pourrait être utilisé dans des contextes variés : médicale (pour les diagnostiques précoces des pathologies), technologique (informatique ubiquitaire), industriel (communication avec des robots), ludique (contrôler un joystick dans les jeux vidéo), etc. Dans notre contexte d’étude, la plateforme ICM proposée a été réalisée pour assister les personnes à mobilité réduite à commander les équipements domestiques. Nous nous sommes intéressés en particulier à l'étude et à l'implémentation des modules de filtrage adaptatif et dynamique, sous forme d'un coprocesseur codé en HDL afin de réduire son temps d'exécution car c'est le bloc le plus critique de la chaine ICM. Quant aux algorithmes d'extraction des caractéristiques et de classification, ils sont exécutés par le processeur Nios-II sous son système d'exploitation en ANSI-C. Le temps de traitement d'un trial par notre système ICM réalisé est de l'ordre de 0.4 s/trial et sa consommation ne dépasse guère 0.7 W. / The main purpose of this thesis is to study and develop an embedded brain computer interface (BCI) system using HW/SW methodology in order to satisfy the system specifications. A complete BCI system integrated in an acquisition system (OpenBCI) and a hardware platform based on the FPGA were achieved. The proposed system can be used in a variety of contexts: medical (for early diagnosis of pathologies, assisting people with severe disabilities to control home devices system through thought), technological (ubiquitous computing), industrial (communication with Robots), games (control a joystick in video games), etc. In our study, the proposed ICM platform was designed to control home devices through the thought of people with severe disabilities. A particular attention has been given to the study and implementation of the filtering module, adaptive and dynamic filtering, in the form of a coprocessor coded in HDL in order to reduce its execution time as it is the critical block in the returned ICM algorithms. For the feature extraction and classification algorithms, they are executed in the Nios-II processor using ANSI-C language. The prototype operates at 200 MHz and performs a real time classification with an execution delay of 0.4 second per trial. The power consumption of the proposed system is about 0.7 W.

L’intégration de l’électronique dans des environnements sévères d’un point de vue électromagnétique a entraîné en contrepartie l’apparition de problèmes de compatibilité électromagnétique (CEM) entre les différents systèmes. Afin d’atteindre un niveau de performance satisfaisant, des tests de sécurité et de certification sont nécessaires. Ces travaux de thèse, réalisés dans le cadre du projet SIMUCEDO (SIMUlation CEM basée sur la norme DO-160), contribuent à la modélisation du test de qualification "Bulk Current Injection" (BCI). Ce test, abordé dans la section 20 dans la norme DO-160 dédiée à l’aéronautique, est désormais obligatoire pour une très grande gamme d’équipements aéronautiques. Parmi les essais de qualification, le test BCI est l’un des plus contraignants et consommateurs du temps. Sa modélisation assure un gain de temps, et une meilleure maîtrise des paramètres qui influencent le passage des tests CEM. La modélisation du test a été décomposée en deux parties : l’équipement sous test (EST) d’une part, et la pince d’injection avec les câbles d’autre part. Dans cette thèse, seul l’EST est pris en compte. Une modélisation "boîte grise" a été proposée en associant un modèle "boîte noire" avec un modèle "extensif". Le modèle boîte noire s’appuie sur la mesure des impédances standards. Son identification se fait avec un modèle en pi. Le modèle extensif permet d’étudier plusieurs configurations de l’EST en ajustant les paramètres physiques. L’assemblage des deux modèles en un modèle boîte grise a été validé sur un convertisseur analogique-numérique (CAN). Une autre approche dénommée approche modale en fonction du mode commun (MC) et du mode différentiel (MD) a été proposée. Elle se base sur les impédances modales du système sous test. Des PCB spécifiques ont été conçus pour valider les équations développées. Une investigation est menée pour définir rigoureusement les impédances modales. Nous avons démontré qu’il y a une divergence entre deux définitions de l’impédance de MC dans la littérature. Ainsi, la conversion de mode (ou rapport Longitudinal Conversion Loss : LCL) a été quantifiée grâce à ces équations. Pour finir, le modèle a été étendu à N-entrées pour représenter un EST de complexité industrielle. Le modèle de l’EST est ensuite associé avec celui de la pince et des câbles travaux réalisés au G2ELAB. Des mesures expérimentales ont été faites pour valider le modèle complet. D’après ces mesures, le courant de MC est impacté par la mise en œuvre des câbles ainsi que celle de l’EST. Il a été montré que la connexion du blindage au plan de masse est le paramètre le plus impactant sur la distribution du courant de MC. / Electronic equipments intended to be integrated in aircrafts are subjected to normative requirements. EMC (Electromagnetic Compatibility) qualification tests became one of the mandatory requirements. This PhD thesis, carried out within the framework of the SIMUCEDO project (SIMulation CEM based on the DO-160 standard), contributes to the modeling of the Bulk Current Injection (BCI) qualification test. Concept, detailed in section 20 in the DO-160 standard, is to generate a noise current via cables using probe injection, then monitor EUT satisfactorily during test. Among the qualification tests, the BCI test is one of the most constraining and time consuming. Thus, its modeling ensures a saving of time, and a better control of the parameters which influence the success of the equipment under test. The modeling of the test was split in two parts : the equipment under test (EUT) on one hand, and the injection probe with the cables on the other hand. This thesis focuses on the EUT modeling. A "gray box" modeling was proposed by associating the "black box" model with the "extensive" model. The gray box is based on the measurement of standard impedances. Its identification is done with a "pi" model. The model, having the advantage of taking into account several configurations of the EUT, has been validated on an analog to digital converter (ADC). Another approach called modal, in function of common mode and differential mode, has been proposed. It takes into account the mode conversion when the EUT is asymmetrical. Specific PCBs were designed to validate the developed equations. An investigation was carried out to rigorously define the modal impedances, in particular the common mode (CM) impedance. We have shown that there is a discrepancy between two definitions of CM impedance in the literature. Furthermore, the mode conversion ratio (or the Longitudinal Conversion Loss : LCL) was quantified using analytical equations based on the modal approach. An N-input model has been extended to include industrial complexity. The EUT model is combined with the clamp and the cables model (made by the G2ELAB laboratory). Experimental measurements have been made to validate the combined model. According to these measurements, the CM current is influenced by the setup of the cables as well as the EUT. It has been shown that the connection of the shield to the ground plane is the most influent parameter on the CM current distribution.

Interfaces cérebro-computador ou Interfaces cérebro-máquina (BCIs/BMIs do inglês Brain-computer interface/Brain-machine interface) são dispositivos que permitem ao usuário interagir com o ambiente ao seu redor sem que seja necessário ativar seus músculos esqueléticos. Estes dispositivos são de extrema valia para indivíduos portadores de deficiências motoras. Esta dissertação ambiciona revisar a literatura acerca de BMIs e expor diferentes técnicas de pré-processamento, extração de características e classificação de sinais neurofisiológicos. Em particular, uma maior ênfase será dada à Máquina de vetor de suporte (SVM do inglês Support-Vector machine), método de classificação baseado no princípio da minimização do risco estrutural. Será apresentado um estudo de caso, que ilustra o funcionamento de uma BMI, a qual permite ao usuário escolher um dentre seis objetos mostrados em uma tela de computador. Esta capacidade da BMI é conseqüência da implementação, através da SVM de um sistema capaz de detectar o potencial evocado P300 nos sinais de eletroencefalograma (EEG). A simulação será realizada em Matlab usando, como sinais de entrada, amostras de EEG de quatro indivíduos saudáveis e quatro deficientes. A análise estatística mostrou que o bom desempenho obtido pela BMI (80,73% de acerto em média) foi promovido pela aplicação da média coerente aos sinais, o que melhorou a relação sinal-ruído do EEG. / Brain-computer interfaces (BCIs) or Brain-machine interfaces (BMIs) technology provide users with the ability to communicate and control their environment without employing normal output pathway of peripheral nerves and muscles. This technology can be especially valuable for highly paralyzed patients. This thesis reviews BMI research, techniques for preprocessing, feature extracting and classifying neurophysiological signals. In particular, emphasis will be given to Support-Vector Machine (SVM), a classification technique, which is based on structural risk minimization. Additionally, a case study will illustrate the working principles of a BMI which analyzes electroencephalographic signals in the time domain as means to decide which one of the six images shown on a computer screen the user chose. The images were selected according to a scenario where users can control six electrical appliances via a BMI system. This was done by exploiting the Support-Vector Machine ability to recognize a specific EEG pattern (the so-called P300). The study was conducted offline within the Matlab environment and used EEG datasets recorded from four disabled and four able-bodied subjects. A statistical survey of the results has shown that the good performance attained (80,73%) was due to signal averaging method, which enhanced EEG signal-to-noise ratio.