92 research outputs found

    A New Generation of Brain-Computer Interface Based on Riemannian Geometry

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    Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first minute of use. They will be reliable, robust and will maintain good performances within and across sessions. A general classification framework based on recent advances in Riemannian geometry and possessing these characteristics is presented. It applies equally well to BCI based on event-related potentials (ERP), sensorimotor (mu) rhythms and steady-state evoked potential (SSEP). The framework is very simple, both algorithmically and computationally. Due to its simplicity, its ability to learn rapidly (with little training data) and its good across-subject and across-session generalization, this strategy a very good candidate for building a new generation of BCIs, thus we hereby propose it as a benchmark method for the field.Comment: 33 pages, 9 Figures, 17 equations/algorithm

    Kessel Run: exploring cooperative behaviours in a multiplayer BCI game

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    Tese de mestrado integrado em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2017Apesar de terem como propósito original o restauro da função a portadores de deficiências motoras, as Interfaces Cérebro-Computador (BCI, do inglês Brain-Computer Interface) têm cada vez mais aplicações que vão para além de controlar o cursor de um computador ou mover uma cadeira de rodas. Com o recente avanço da tecnologia de electroencefalografia (EEG), cada vez mais portátil e económica, a investigação na área dos BCI tem nos últimos anos dado maior destaque às aplicações para utilizadores saudáveis, nomeadamente na área do entretenimento. BCI baseados em EEG estão gradualmente a ser mais usados até mesmo em jogos comerciais. Os videojogos do género multijogador são extremamente populares entre os jogadores, pelo que se torna bastante interessante olhar para os jogos multi-cérebro, isto é, jogos onde de uma ou outra forma a atividade cerebral de mais do que um utilizador é analisada e necessária para jogar o jogo. Num outro tópico de investigação, as medições de EEG são também usadas por neurocientistas na pesquisa convencional dos processos de tomada de decisão e raciocínio estratégico. Um dos paradigmas mais frequentemente utilizados para estudar a tomada de decisão _e o uso de dilemas da teoria de jogos jogados por uma ou duas pessoas. A teoria de jogos é aplicada a uma panóplia de áreas que vão desde a economia à psicologia, podendo naturalmente ser aplicável aos videojogos cooperativos ou competitivos. Conseguiremos extrair novos conhecimentos acerca da neurociência da tomada de decisão a partir de jogos BCI multijogador? E, por exemplo, será possível manipular a estratégia de um jogador fornecendo-lhe informação sobre o que vai na mente dos seus adversários? O objetivo desta dissertação é explorar os comportamentos cooperativos que ocorrem entre jogadores num jogo BCI multijogador, bem como enquanto jogam dilemas clássicos da teoria de jogos. Ao investigar medidas neurológicas correlacionadas com o raciocínio estratégico tais como os potenciais evocados (ERP, do inglês Event-Related Potentials) durante decisões cooperativas ou desertoras, procuramos aplicar os conhecimentos da pesquisa em tomada de decisão aos jogos digitais de classe comercial. Numa primeira etapa deste trabalho, foi desenhado e implementado um jogo BCI cooperativo baseado no paradigma SSVEP (do inglês Steady-State Visually-Evoked Potential) chamado Kessel Run. No jogo Kessel Run, dois jogadores devem trabalhar juntos de forma a pilotar uma nave espacial através de um campo de asteróides. O objetivo do jogo é finalizar uma corrida de 2 minutos sem perder todo o combustível, desviando-se de obstáculos e recolhendo bónus. O paradigma de interacção SSVEP foi implementado usando dois painéis LED externos, acoplados ao monitor, permitindo aos jogadores mover a nave para cima ou para baixo ao olhar para as luzes, a piscar a uma frequência de 15 e 12 Hz, respetivamente. Dado que uma das nossas principais motivações era desenhar um jogo BCI que não fosse simplesmente uma prova de conceito da tecnologia, mas também divertido para os jogadores, foram seguidos os requerimentos para um bom design de jogo. Desta forma, o jogo Kessel Run apresenta regras e objetivos claros, mantendo-se desafiante para os jogadores, com o desafio adicional de controlar o BCI. Para além disso, de forma a proporcionar a experiência cooperativa adequada, os dois jogadores tinham funções interdependentes ditadas pelas mecânicas de jogo, uma vez que um jogador só consegue controlar um motor da nave, e esta só pode subir ou descer quando ambos os jogadores a controlam ao mesmo tempo. Para os ajudar a alcançar a vitória mútua, os jogadores podem comunicar verbalmente para antecipar obstáculos e melhor controlar o jogo. Na segunda etapa deste trabalho, foi desenhado o jogo Dilemmas: um conjunto de cinco dilemas sociais iterados habitualmente utilizados na teoria de jogos. Em cada jogo, os jogadores enfrentam uma escolha entre duas opções: cooperar com o outro jogador ou desertar. A combinação de ambas as decisões resulta num de quatro desfechos possíveis, cada um com diferentes consequências para cada jogador, representados por uma pontuação numérica. Para cada jogo, um jogador ganha uma ronda quando recebe mais pontos do que o adversário, mas os jogadores tanto podem tentar maximizar a sua pontuação pessoal para derrotar o adversário como tentar maximizar a pontuação do grupo ao tomar decisões que beneficiam ambos os jogadores igualmente. O jogo Dilemmas tem o propósito de servir como um ambiente controlado que nos permita recolher dados da atividade cerebral durante decisões cooperativas e desertoras. Os participantes tomam as suas decisões recorrendo ao teclado e não há qualquer comunicação permitida, como a finalidade de reduzir artefactos devidos ao movimento e ruído no sinal. Foram analisados os ERPs no sinal de EEG marcado no tempo no momento que antecede a tomada de decisão e após a apresentação do desfecho de cada ronda do jogo. Após implementar ambos os jogos, foi preparada uma experiência onde 12 participantes (em 6 pares) foram convidados a usar toucas EEG enquanto jogavam Kessel Run, seguido de Dilemmas. A performance do BCI durante o Kessel Run foi calculado através de uma sessão de treino antes do jogo começar. A experiência de jogo e social dos participantes foi também estudada, com recurso a questionários validados preenchidos após cada sessão de jogo. Foi realizada a análise da atividade cerebral registada durante ambos os jogos, onde foram estudados os ERPs com origem no córtex medial frontal, nomeadamente as componentes P300 e a negatividade relativa a feedback (FRN, do inglês Feedback-Related Negativity). A performance do paradigma SSVEP no BCI foi mais baixa do que o esperado, alcançando apenas uma precisão máxima de 79% como precisão média geral de 55% para um nível de chance de 33%. Os dois fatores identificados que mais influenciaram este resultado foram a variabilidade na deteção da frequência de SSVEP entre sujeitos e a falta de escuridão na sala. A maioria dos participantes obteve piores resultados de classificação para a frequência de 15 Hz do que para 12 Hz, possivelmente devido a 12 Hz pertencer à banda alfa dominante. Embora funcione como prova de conceito para um jogo SSVEP multijogador, um paradigma mais intuitivo como o movimento imaginado pode ser mais adequado para o Kessel Run, permitindo aos jogadores manter o olhar no ecrã. A experiência reportada pelos jogadores foi de forma geral positiva, apesar da dificuldade em controlar o jogo com o paradigma SSVEP. Os jogadores não se sentiram muito competentes durante o jogo, mas de qualquer maneira atingiram um estado de Flow. Isto pode dever-se à estratégia colaborativa desenvolvida por alguns jogadores para contornar a má classificação SSVEP, em que o jogador com melhor controlo controlava a nave enquanto o companheiro dava direções. Na avaliação da presença social, os jogadores reportaram que empatizaram com o outro, em parte devido à necessidade de comunicar para ganhar o jogo. Dado que os jogadores se sentiram inclinados a trabalhar com o outro, podemos dizer que as regras de design de jogo cooperativo foram implementadas com sucesso e o jogo proporcionou uma experiência social positiva. No jogo Dilemmas, a presença social reportada pelos jogadores foi ligeiramente diferente, resultado da natureza contrastante do jogo. Neste jogo, o nível de familiaridade dos dois participantes em cada sessão influenciou fortemente a forma como jogaram. Participantes emparelhados com um desconhecido sentiram-se menos inclinados a cooperar, e tomaram uma abordagem mais competitiva ao jogo, sentindo menos empatia pelo outro. Os jogadores reportaram também mais sentimentos negativos durante o Dilemmas do que durante o Kessel Run, embora tal se deva talvez às rondas perdidas e não à interação com o companheiro. A estratégia tit-for-tat (olho por olho) foi a mais adoptada pelos jogadores, o que significa que começavam por cooperar e subsequentemente replicavam a decisão feita pelo adversário na ronda anterior. No que respeita ao estudo de ERPs durante o jogo, começou por se analisar os dados recolhidos durante o Kessel Run. Os registos acabaram por ser demasiado ruidosos para se extrair alguma informação sobre os potenciais que antecedem a tomada de decisão. As mecânicas e comandos do jogo não favoreceram a recolha de dados EEG para esta análise, uma vez que os jogadores eram encorajados a falar e mover a cabeça para olhar para as fontes de luz de forma a controlar o jogo. A implementação de um paradigma de interação passivo pode possibilitar este estudo num jogo BCI. Por outro lado, foram identificadas com sucesso duas componentes ERP marcadas no tempo em relação à apresentação do desfecho no jogo Dilemmas: o P300 e a FRN. O ambiente mais controlado deste jogo facilitou a deteção de uma forte positividade na região medial frontal, para os canais Fc1, Fc2, Fz e Cz. Esta positividade corresponde às características da componente P300, uma deexão positiva no ERP, relacionada com o processamento de informação acerca de ganhos e perdas. O P300 foi observado entre 200 e 500 ms após a apresentação do desfecho dos jogos aos jogadores. A componente FRN foi também detetada, embora apenas nos ensaios em que os jogadores cooperaram e perderam nessa ronda. A FRN foi identificada de 200 a 250 ms após o estímulo visual (desfecho), correspondendo a situações em que os jogadores adotaram a estratégia tit-for-tat, particularmente comum entre participantes que não se conhecem. Um jogador que coopera e recebe um desfecho negativo (perde a ronda) tem maiores probabilidades de desertar na ronda seguinte, repetindo o comportamento prévio do adversário. Os resultados alcançados neste trabalho ajudam-nos a compreender a dificuldade em adquirir dados EEG durante uma experiência de jogo BCI ativa. Para atingir uma deteção adequada de ERPs durante um jogo, é necessário desenvolver algoritmos mais robustos de forma a ultrapassar a presença de artefactos. Todavia, as aplicações dos correlatos neuronais de tomada de decisão em jogos parecem promissoras, sobretudo em jogos sérios e jogos multijogador.Traditional brain-computer interface (BCI) research has recently turned to applications for healthy users, such as games. Because electroencephalography (EEG) is a cheap, portable and popular way of accessing brain activity, EEG-based BCIs are gradually being more used even for commercial games. Multiplayer games are immensely popular among gamers, so it becomes interesting to look at `multi-brain games', that is, games where in one or other form the measured brain activity of more than one user is needed to play the game. On a different research topic, EEG measures are also used by neuroscientists in traditional decision-making and strategic reasoning research. One of the most common paradigms used to study decision making is to use game theory dilemmas played by one or two persons. Game theory is applied to a myriad of areas from economics to psychology, and can of course be applicable to cooperative or competitive video games. The goal of this dissertation is to explore the cooperative behaviors that happen between players in a multiplayer BCI game, as well as while playing classic game theory dilemmas. By looking at neural correlates of strategic reasoning such as event-related potentials (ERPs) during cooperative or defective decisions, we will try to bring decision-making research and insights to commercial grade digital games. We have divided this work's methodology into two parts: firstly, an original two player cooperative BCI game (Kessel Run) controlled with steady-state visually evoked potentials (SSVEP) was conceptualized and developed; secondly, a non-BCI game inspired by iterated social dilemmas was also developed. We have designed and set-up an experiment where participants played both games sequentially, and have collected EEG data during both gaming experiences, as well as the reported game experience and social presence from participant-filled questionnaires. Despite a lower than expected accuracy in the BCI paradigm used to control the game Kessel Run (maximum of 79% and 55% on average), participants adjusted and developed strategies to successfully navigate a spaceship together in a virtual environment, reporting a positive game experience. Studying ERPs while playing Kessel Run proved ineffective, due to the fast pacing of the game and movement artefacts caused by the SSVEP paradigm. However, in a more controlled setting like the game Dilemmas, we have successfully identified two components heavily linked to information processing and decision-making. A strong medial frontal positivity corresponding to the P300 component was observed between 200 and 500ms after the presentation of game outcomes to the player. In trials where players cooperated and lost the round, the feedback-related negativity (FRN) was also detected, as would be expected when participants fail to achieve a desired feedback. Designing a BCI game that employs the P300 paradigm might improve the success of merging decision-making neural correlates in a gaming experience. Never the less, the insights gathered in this study made us understand the difficulty of collecting EEG data during active BCI game play. Still, an interesting prospect would be to use a subject's particular brainwaves as a means to decode future decisions, and in that way improve collaboration in a game or team activity

    The N400 for Brain Computer Interfacing: complexities and opportunities

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    The N400 is an Event Related Potential that is evoked in response to conceptually meaningful stimuli. It is for instance more negative in response to incongruent than congruent words in a sentence, and more negative for unrelated than related words following a prime word. This sensitivity to semantic content of a stimulus in relation to the mental context of an individual makes it a signal of interest for Brain Computer Interfaces. Given this potential it is notable that the BCI literature exploiting the N400 is limited. We identify three existing application areas: (1) exploiting the semantic processing of faces to enhance matrix speller performance, (2) detecting language processing in patients with Disorders of Consciousness, and (3) using semantic stimuli to probe what is on a user's mind. Drawing on studies from these application areas, we illustrate that the N400 can successfully be exploited for BCI purposes, but that the signal-to-noise ratio is a limiting factor, with signal strength also varying strongly across subjects. Furthermore, we put findings in context of the general N400 literature, noting open questions and identifying opportunities for further research.Comment: 28 pages, 2 figures, 2 table

    Enhancement and optimization of a multi-command-based brain-computer interface

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    Brain-computer interfaces (BCI) assist disabled person to control many appliances without any physically interaction (e.g., pressing a button). SSVEP is brain activities elicited by evoked signals that are observed by visual stimuli paradigm. In this dissertation were addressed the problems which are oblige more usability of BCI-system by optimizing and enhancing the performance using particular design. Main contribution of this work is improving brain reaction response depending on focal approaches

    Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data

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    Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and heavy structures with the risk of overfitting small datasets, and architectures are often defined empirically. Furthermore, CNNs are mainly validated by designing within-subject decoders. Crucially, the automatically learned features mainly remain unexplored; conversely, interpreting these features may be of great value to use decoders also as analysis tools, highlighting neural signatures underlying the different decoded brain or behavioral states in a data-driven way. Lastly, SOA DL-based algorithms used to decode single-cell recordings rely on more complex, slower to train and less interpretable networks than CNNs, and the use of CNNs with these signals has not been investigated. This PhD research addresses the previous limitations, with reference to P300 and motor decoding from EEG, and motor decoding from single-neuron activity. CNNs were designed light, compact, and interpretable. Moreover, multiple training strategies were adopted, including transfer learning, which could reduce training times promoting the application of CNNs in practice. Furthermore, CNN-based EEG analyses were proposed to study neural features in the spatial, temporal and frequency domains, and proved to better highlight and enhance relevant neural features related to P300 and motor states than canonical EEG analyses. Remarkably, these analyses could be used, in perspective, to design novel EEG biomarkers for neurological or neurodevelopmental disorders. Lastly, CNNs were developed to decode single-neuron activity, providing a better compromise between performance and model complexity

    Interfacce cervello-computer per la comunicazione aumentativa: algoritmi asincroni e adattativi e validazione con utenti finali

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    This thesis aimed at addressing some of the issues that, at the state of the art, avoid the P300-based brain computer interface (BCI) systems to move from research laboratories to end users’ home. An innovative asynchronous classifier has been defined and validated. It relies on the introduction of a set of thresholds in the classifier, and such thresholds have been assessed considering the distributions of score values relating to target, non-target stimuli and epochs of voluntary no-control. With the asynchronous classifier, a P300-based BCI system can adapt its speed to the current state of the user and can automatically suspend the control when the user diverts his attention from the stimulation interface. Since EEG signals are non-stationary and show inherent variability, in order to make long-term use of BCI possible, it is important to track changes in ongoing EEG activity and to adapt BCI model parameters accordingly. To this aim, the asynchronous classifier has been subsequently improved by introducing a self-calibration algorithm for the continuous and unsupervised recalibration of the subjective control parameters. Finally an index for the online monitoring of the EEG quality has been defined and validated in order to detect potential problems and system failures. This thesis ends with the description of a translational work involving end users (people with amyotrophic lateral sclerosis-ALS). Focusing on the concepts of the user centered design approach, the phases relating to the design, the development and the validation of an innovative assistive device have been described. The proposed assistive technology (AT) has been specifically designed to meet the needs of people with ALS during the different phases of the disease (i.e. the degree of motor abilities impairment). Indeed, the AT can be accessed with several input devices either conventional (mouse, touchscreen) or alterative (switches, headtracker) up to a P300-based BCI.Questa tesi affronta alcune delle problematiche che, allo stato dell'arte, limitano l'usabilità delle interfacce cervello computer (Brain Computer Interface - BCI) al di fuori del contesto sperimentale. E' stato inizialmente definito e validato un classificatore asincrono. Quest'ultimo basa il suo funzionamento sull'inserimento di un set di soglie all'interno del classificatore. Queste soglie vengono definite considerando le distribuzioni dei valori di score relativi agli stimoli target e non-target e alle epoche EEG in cui il soggetto non intendeva effettuare nessuna selezione (no-control). Con il classificatore asincrono, un BCI basato su potenziali P300 può adattare la sua velocità allo stato corrente dell'utente e sospendere automaticamente il controllo quando l'utente non presta attenzione alla stimolazione. Dal momento che i segnali EEG sono non-stazionari e mostrano una variabilità intrinseca, al fine di rendere possibile l'utilizzo dei sistemi BCI sul lungo periodo, è importante rilevare i cambiamenti dell'attività EEG e adattare di conseguenza i parametri del classificatore. A questo scopo, il classificatore asincrono è stato successivamente migliorato introducendo un algoritmo di autocalibrazione per la continua e non supervisionata ricalibrazione dei parametri di controllo soggettivi. Infine è stato definito e validato un indice per monitorare on-line la qualità del segnale EEG, in modo da rilevare potenziali problemi e malfunzionamenti del sistema. Questa tesi si conclude con la descrizione di un lavoro che ha coinvolto gli utenti finali (persone affette da sclerosi laterale amiotrofica-SLA). In particolare, basandosi sui principi dell’user-centered design, sono state descritte le fasi relative alla progettazione, sviluppo e validazione di una tecnologia assistiva (TA) innovativa. La TA è stata specificamente progettata per rispondere alla esigenze delle persone affetta da SLA durante le diverse fasi della malattia. Infatti, la TA proposta può essere utilizzata sia mediante dispositivi d’input tradizionali (mouse, tastiera) che alternativi (bottoni, headtracker) fino ad arrivare ad un BCI basato su potenziali P300

    TOWARDS STEADY-STATE VISUALLY EVOKED POTENTIALS BRAIN-COMPUTER INTERFACES FOR VIRTUAL REALITY ENVIRONMENTS EXPLICIT AND IMPLICIT INTERACTION

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    In the last two decades, Brain-Computer Interfaces (BCIs) have been investigated mainly for the purpose of implementing assistive technologies able to provide new channels for communication and control for people with severe disabilities. Nevertheless, more recently, thanks to technical and scientific advances in the different research fields involved, BCIs are gaining greater attention also for their adoption by healthy users, as new interaction devices. This thesis is dedicated to to the latter goal and in particular will deal with BCIs based on the Steady State Visual Evoked Potential (SSVEP), which in previous works demonstrated to be one of the most flexible and reliable approaches. SSVEP based BCIs could find applications in different contexts, but one which is particularly interesting for healthy users, is their adoption as new interaction devices for Virtual Reality (VR) environments and Computer Games. Although being investigated since several years, BCIs still poses several limitations in terms of speed, reliability and usability with respect to ordinary interaction devices. Despite of this, they may provide additional, more direct and intuitive, explicit interaction modalities, as well as implicit interaction modalities otherwise impossible with ordinary devices. This thesis, after a comprehensive review of the different research fields being the basis of a BCI exploiting the SSVEP modality, present a state-of-the-art open source implementation using a mix of pre-existing and custom software tools. The proposed implementation, mainly aimed to the interaction with VR environments and Computer Games, has then been used to perform several experiments which are hereby described as well. Initially performed experiments aim to stress the validity of the provided implementation, as well as to show its usability with a commodity bio-signal acquisition device, orders of magnitude less expensive than commonly used ones, representing a step forward in the direction of practical BCIs for end users applications. The proposed implementation, thanks to its flexibility, is used also to perform novel experiments aimed to investigate the exploitation of stereoscopic displays to overcome a known limitation of ordinary displays in the context of SSVEP based BCIs. Eventually, novel experiments are presented investigating the use of the SSVEP modality to provide also implicit interaction. In this context, a first proof of concept Passive BCI based on the SSVEP response is presented and demonstrated to provide information exploitable for prospective applications

    Brain computer interfaces: an engineering view. Design, implementation and test of a SSVEP-based BCI.

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    This thesis presents the realization of a compact, yet flexible BCI platform, which, when compared to most commercially-available solution, can offer an optimal trade-off between the following requirements: (i) minimal, easy experimental setup; (ii) flexibility, allowing simultaneous studies on other bio-potentials; (iii) cost effectiveness (e.g. < 1000 €); (iv) robust design, suitable for operation outside lab environments. The thesis encompasses all the project phases, from hardware design and realization, up to software and signal processing. The work started from the development of the hardware acquisition unit. It resulted in a compact, battery-operated module, whose medium-to-large scale production costs are in the range of 300 €. The module features 16 input channels and can be used to acquire different bio-potentials, including EEG, EMG, ECG. Module performance is very good (RTI noise < 1.3 uVpp), and was favourably compared against a commercial device (g.tec USBamp). The device was integrated into an ad-hoc developed Matlab-based platform, which handles the hardware control, as well as the data streaming, logging and processing. Via a specifically developed plug-in, incoming data can also be streamed to a TOBI-interface compatible system. As a demonstrator, the BCI was developed for AAL (Ambient Assisted Living) system-control purposes, having in mind the following requirements: (i) online, self-paced BCI operation (i.e., the BCI monitors the EEG in real-time and must discern between intentional control periods, and non-intentional, rest ones, interpreting the user’s intent only in the first case); (ii) calibration-free approach (“ready-to-use”, “Plug&Play”); (iii) subject-independence (general approach). The choice of the BCI operating paradigm fell on Steady State visual Evoked Potential (SSVEP). Two offline SSVEP classification algorithms were proposed and compared against reference literature, highlighting good performance, especially in terms of lower computational complexity. A method for improving classification accuracy was presented, suitable for use in online, self-paced scenarios (since it can be used to discriminate between intentional control periods and non-intentional ones). Results show a very good performance, in particular in terms of false positives immunity (0.26 min^-1), significantly improving over the state of the art. The whole BCI setup was tested both in lab condition, as well as in relatively harsher ones (in terms of environmental noise and non-idealities), such as in the context of the Handimatica 2014 exhibition. In both cases, a demonstrator allowing control of home appliances through BCI was developed

    Development and applications of a smartphone-based mobile electroencephalography (EEG) system

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    Electroencephalography (EEG) is a clinical and research technique used to non-invasively acquire brain activity. EEG is performed using static systems in specialist laboratories where participant mobility is constrained. It is desirable to have EEG systems which enable acquisition of brain activity outside such settings. Mobile systems seek to reduce the constraining factors of EEG device and participant mobility to enable recordings in various environments but have had limited success due to various factors including low system specification. The main aim of this thesis was to design, build, test and validate a novel smartphone-based mobile EEG system.A literature review found that the term ‘mobile EEG’ has an ambiguous meaning as researchers have used it to describe many differing degrees of participant and device mobility. A novel categorisation of mobile EEG (CoME) scheme was derived from thirty published EEG studies which defined scores for participant and device mobilities, and system specifications. The CoME scheme was subsequently applied to generate a specification for the proposed mobile EEG system which had 24 channels, sampled at 24 bit at a rate of 250 Hz. Unique aspects of the EEG system were the introduction of a smartphone into the specification, along with the use of Wi-Fi for communications. The smartphone’s processing power was used to remotely control the EEG device so as to enable EEG data capture and storage as well as electrode impedance checking via the app. This was achieved by using the Unity game engine to code an app which provided the flexibility for future development possibilities with its multi-platform support.The prototype smartphone-based waist-mounted mobile EEG system (termed ‘io:bio’) was validated against a commercial FDA clinically approved mobile system (Micromed). The power spectral frequency, amplitude and area of alpha frequency waves were determined in participants with their eyes closed in various postures: lying, sitting, standing and standing with arms raised. Since a correlation analysis to compare two systems has interpretability problems, Bland and Altman plots were utilised with a priori justified limits of agreement to statistically assess the agreement between the two EEG systems. Overall, the results found similar agreements between the io:bio and Micromed systems indicating that the systems could be used interchangeably. Utilising the io:bio and Micromed systems in a walking configuration, led to contamination of EEG channels with artifacts thought to arise from movement and muscle-related sources, and electrode displacement.To enable an event related potential (ERP) capability of the EEG system, additional coding of the smartphone app was undertaken to provide stimulus delivery and associated data marking. Using the waist-mounted io:bio system, an auditory oddball paradigm was also coded into the app, and delivery of auditory tones (standard and deviant) to the participant (sitting posture) achieved via headphones connected to the smartphone. N100, N200 and P300 ERP components were recorded in participants sitting, and larger amplitudes were found for the deviant tones compared to the standard ones. In addition, when the paradigm was tested in individual participants during walking, movement-related artifacts impacted negatively upon the quality of the ERP components, although components were discernible in the grand mean ERP.The io:bio system was redesigned into a head-mounted configuration in an attempt to reduce EEG artifacts during participant walking. The initial approach taken to redesign the system involved using electronic components populated onto a flexible PCB proved to be non-robust. Instead, the rigid PCB form of the circuitry was taken from the io:bio waist-mounted system and placed onto the rear head section of the electrode cap via a bespoke cradle. Using this head-mounted system, in a preliminary auditory oddball paradigm study, ERP responses were obtained in participants whilst walking. Initial results indicate that artifacts are reduced in this head-mounted configuration, and N100, N200 and P300 components are clearly identifiable in some channels

    Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness

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    In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. Darüber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes für zwei innovative BCI Paradigmen, für die es bisher keine etablierte Mustererkennungsmethodik gibt
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