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.

Tato diplomová práce představuje koncept elektrického invalidního vozíku ovládaného lidskou myslí. Tento koncept je určen pro osoby, které elektrický invalidní vozík nemohou ovládat klasickými způsoby, jakým je například joystick. V práci jsou popsány čtyři hlavní komponenty konceptu: elektroencefalograf, brain-computer interface (rozhraní mozek-počítač), systém sdílené kontroly a samotný elektrický invalidní vozík. V textu je představena použitá metodologie a výsledky provedených experimentů. V závěru jsou nastíněna doporučení pro budoucí vývoj.

<p>Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel. </p><p>This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication. </p><p>In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.</p> / Dissertation

This thesis analyzes the market and products of brain-computer interface. Its main goal is evaluate the current market of these products and their use in information technologies and systems. The document is divided into three main parts. The first one focuses on familiarizing the reader with brain-computer interface technology. It mentions a brief history of development, technological principles, types of devices, their contemporary use, and the positives and negatives they bring. The second one focuses on market analysis. It summarizes the active companies on the market, and their products, and describes their customer targeting. It contains brief insight in market's future development. The third part focuses on practical testing of two selected brain-computer interface devices. The testing will evaluate applicability in information technologies and systems. For testing purpose will be developed its own methodology and selected appropriate evaluation criteria.

<p>A series of fusion techniques are developed and applied to EEG and pupillary recording analysis in a rapid serial visual presentation (RSVP) based image triage task, in order to improve the accuracy of capturing single-trial neural/pupillary signatures (patterns) associated with visual target detection.</p><p>The brain response to visual stimuli is not a localized pulse, instead it reflects time-evolving neurophysiological activities distributed selectively in the brain. To capture the evolving spatio-temporal pattern, we divide an extended (``global") EEG data epoch, time-locked to each image stimulus onset, into multiple non-overlapping smaller (``local") temporal windows. While classifiers can be applied on EEG data located in multiple local temporal windows, outputs from local classifiers can be fused to enhance the overall detection performance.</p><p>According to the concept of induced/evoked brain rhythms, the EEG response can be decomposed into different oscillatory components and the frequency characteristics for these oscillatory components can be evaluated separately from the temporal characteristics. While the temporal-based analysis achieves fairly accurate detection performance, the frequency-based analysis can improve the overall detection accuracy and robustness further if frequency-based and temporal-based results are fused at the decision level.</p><p>Pupillary response provides another modality for a single-trial image triage task. We developed a pupillary response feature construction and selection procedure to extract/select the useful features that help to achieve the best classification performance. The classification results based on both modalities (pupillary and EEG) are further fused at the decision level. Here, the goal is to support increased classification confidence through inherent modality complementarities. The fusion results show significant improvement over classification results using any single modality.</p><p>For crucial image triage tasks, multiple image analysts could be asked to evaluate the same set of images to improve the probability of detection and reduce the probability of false positive. We observe significant performance gain by fusing the decisions drawn by multiple analysts.</p><p>To develop a practical real-time EEG-based application system, sometimes we have to work with an EEG system that has a limited number of electrodes. We present methods of ranking the channels, identifying a reduced set of EEG channels that can deliver robust classification performance.</p> / Dissertation

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 describes a brain-computer interface (BCI) system that was developed to allow direct cortical control of 7 active degrees of freedom in a robotic arm. Two monkeys with chronic microelectrode implants in their motor cortices were able to use the arm to complete an oriented grasping task under brain control. This BCI system was created as a clinical prototype to exhibit (1) simultaneous decoding of cortical signals for control of the 3-D translation, 3-D rotation, and 1-D finger aperture of a robotic arm and hand, (2) methods for constructing cortical signal decoding models based on only observation of a moving robot, (3) a generalized method for training subjects to use complex BCI prosthetic robots using a novel form of operator-machine shared control, and (4) integrated kinematic and force control of a brain-controlled prosthetic robot through a novel impedance-based robot controller. This dissertation describes each of these features individually, how their integration enriched BCI control, and results from the monkeys operating the resulting system.

The work deals with areas of brain--computer interface (BCI). There is theoretical background described because of the research in the first part. The chapter "Analýza technologie (EEG, EMG)" is conceived generally and clarifies basic theory of EEG and EMG. Chapter "Popis zařízení EPOC neuroheadset" examines specific device used in research especially on the technical and functional side. Section "Analýza praktického využití BCI zařízení Emotiv EPOC neuroheadset" is self-explanatory. The focus of the practical part is influence of factors on the usability of BCI neuroheadset EPOC in the field of information management. These factors have been organized and analyzed. Group of factors connected with humans (physical and psychical) was chosen for the application of the research in which was investigated correlation with the ability to learn how to use neuroheadset EPOC, respectively its BCI element. For the research was used experimental method when a sample of volunteers was tested, undergone questionnaire investigation for acquiring human factors and repeatedly tested for the ability to use BCI element of neuroheadset EPOC. There was found out that the ability to learn how to use BCI correlates with optimism (Pearson's correlation coefficient 0,731 [Pkk] on the level of significance 0,01), stability (|0,648| Pkk on the level of significance 0,01), concentration (|0,638| Pkk on the level of significance 0,01 ), self-efficacy (0,549 Pkk on the level of significance 0,05), spatial perception (0,426 Pkk on the level of significance 0,01) in the research part.

This thesis describes recording, processing and classifying brain activity which is being captured by a brain-computer interface (BCI) device manufactured by OpenBCI company. Possibility of use of such a device for controlling an application with brain activity, specifically with thinking of left or right hand movement, is discussed. To solve this task methods of signal processing and machine learning are used. As a result a program that is capable of recording, processing and classifying brain activity using an artificial neural network is created. An average accuracy of classification of synthetic data is 99.156%. An average accuracy of classification of real data is 73.71%.

In recent years, brain computer interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by current practices used in BCI App stores and then describe some countermeasures that could be used to mitigate the privacy threats. Also, develop a prototype which gives the BCI app users a choice to restrict their brain signal dynamically.