This work began with a review of visual spatial selective attention, from a behavioural perspective with particular emphasis placed upon the spotlight model. To complement the behavioural review, the physiological aspects of the visual system were studied to find possible loci of the spotlight. The literature pointed to the pulvinar nucleus of the thalamus, interacting with the parietal and frontal cortices. Some experimental work examined relationships between visual spatial selective attention and event-related potentials (ERPs) recorded from the scalp. The second section of this thesis reviewed the ERP measures relating specifically to the visual modality for their possible application in a visual attentional task. This yielded two independent findings. First, the Probe-ERP paradigm comprising an attentional task being performed by the subject, with a separate stimulus to probe the unused resources within the system. Second, the steady-state evoked response, with the stimulus presented as a small sinusoidal variation around a mean level of contrast. The combination of the Probe-ERP paradigm and the steady-state visually evoked potential (SSVEP) warranted experimental evaluation.

Made available in DSpace on 2018-08-02T00:01:12Z (GMT). No. of bitstreams: 1 tese_8765_dissertacao Alan Silva da Paz Floriano20160331-161919.pdf: 7979628 bytes, checksum: 1cb1c50222a24c607e72086a05555b0a (MD5) Previous issue date: 2016-03-11 / Uma parcela da população é composta por pessoas que são acometidas de doenças ou vítimas de acidentes graves que as impossibilitam de interagir e se comunicar. Novas tecnologias têm surgido para prover a essas pessoas um canal de comunicação alternativo através de sinais cerebrais. Esses sistemas são conhecidos como Interfaces Cérebro-Computador (ICCs). Este trabalho descreve o desenvolvimento de uma ICC baseada no paradigma de Potenciais Evocados Visuais de Regime Permanente (Steady State Visual Evoked Potential - SSVEP) aplicada à Comunicação Alternativa e Robô de Telepresença. A interface foi construída para quatro comandos de seleção atráves de estímulos visuais desenvolvidos em um software utilizando a biblioteca gráfica OpenGL e executados em frequências distintas (5,6Hz, 6,4Hz, 6,9Hz e 8,0Hz). Todos os voluntários avaliados nos testes utilizando o sistema online conseguiram completar as tarefas propostas com uma taxa de acerto média de 88,3% ± 5,4%, tempo de classificação de 5,6s ± 0,5s e ITR média de 14,2 bits/min ± 3,5 bits/min, não necessitando de treinamento e utilizando apenas um canal para aquisição do sinal eletroencefalográfico. Os resultados demonstraram a possibilidade da construção de uma ICC que poderá ser utilizada nos futuros projetos de tecnologias assistivas desenvolvidos no Laboratório de Automação Inteligente da Universidade Federal do Espírito Santo (LAI-UFES).

Made available in DSpace on 2018-08-02T00:01:45Z (GMT). No. of bitstreams: 1 tese_10281_TeseDoutoradoRichardTello2016.pdf: 12331551 bytes, checksum: 0dae4547527893319ca299b5e22f6234 (MD5) Previous issue date: 2016-09-01 / Durante os últimos dez anos, as Interfaces Cérebro Computador (ICC) baseadas em Potenciais Evocados Visuais de Regime Permanente (SSVEP) têm chamado a atenção de muitos pesquisadores devido aos resultados promissores e as altas taxas de precisão atingidas. Este tipo de ICC permite que pessoas com dificuldades motoras severas possam se comunicar com o mundo exterior através da modulação da atenção visual a luzes piscantes com frequência determinada. Esta Tese de Doutorado tem o intuito de desenvolver um novo enfoque dentro das chamadas ICC Independentes, nas quais os usuários não necessitam executar tarefas neuromusculares para seleção visual de objetivos específicos, característica que a distingue das tradicionais ICCs-SSVEP. Assim, pessoas com difculdades motoras severas, como pessoas com Esclerose Lateral Amiotrófca (ELA), contam com uma nova alternativa de se comunicar através de sinais cerebrais. Diversas contribuições foram realizadas neste trabalho, como, por exemplo, melhoria do algoritmo extrator de características, denominado Índice de Sincronização Multivariável (ou MSI, do Inglês), para a detecção de potenciais evocados; desenvolvimento de um novo método de detecção de potenciais evocados através da correlação entre modelos multidimensionais (tensores); o desenvolvimento do primeiro estudo sobre a influência de estímulos coloridos na detecção de SSVEPs usando LEDs; a aplicação do conceito de Compressão na detecção de SSVEPs; e, fnalmente, o desenvolvimento de uma nova ICC independente que utiliza o enfoque de Percepção Fundo-Figura (ou FGP, do Inglês).

Submitted by JÚLIO HEBER SILVA (julioheber@yahoo.com.br) on 2018-03-13T17:24:13Z No. of bitstreams: 2 Dissertação - Andrei Damian da Silva - 2018.pdf: 2168750 bytes, checksum: 4d47d811f294faae439470b427c48f3e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-03-14T10:57:00Z (GMT) No. of bitstreams: 2 Dissertação - Andrei Damian da Silva - 2018.pdf: 2168750 bytes, checksum: 4d47d811f294faae439470b427c48f3e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-03-14T10:57:00Z (GMT). No. of bitstreams: 2 Dissertação - Andrei Damian da Silva - 2018.pdf: 2168750 bytes, checksum: 4d47d811f294faae439470b427c48f3e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-03-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This paper presents a new approach for the processing and classification of visual evoked potentials of steady state (SSVEP). It introduces a ensemble tree model that combines canonical correlation analysis data with methods based on estimation of power spectral density. The stimuli were created using LEDs, from 7.04 Hz to 38.46 Hz. Data were collected using the Texas Instruments ADS1299EEG-Fe and three electrodes. The tests were performed for different distances and light intensities to evaluate the performance of the algorithm under different conditions. In all, 22 participants were recruited, and the average classification was 99.1 ± 2.27% with fixed decision time of 1 second. / Este trabalho apresenta uma nova abordagem para o processamento e classificação de potenciais evocados visuais de estado estacionário (SSVEP). Este trabalho introduz um modelo de em aprendizagem por agrupamento de árvores de decisão que combina dados de análise da correlação canônica com métodos baseados na estimativa da densidade espectral de potência. Os estímulos foram criados utilizando LEDs, com frequência de 7.04 Hz até 38.46 Hz. Os dados foram coletados utilizando a placa ADS1299EEG-Fe da Texas Instruments e três eletrodos. Os testes foram realizados para diferentes distâncias e intensidades luminosas com o objetivo de avaliar o desempenho do algoritmo em condições diversas. Ao todo, 22 participantes foram recrutados e a taxa de acertos média foi de 99.1±2.27% com tempo de decisão fixo em 1 segundo.

Cette thèse porte sur la conception et l'évaluation de systèmes interactifs utilisant des interfaces cerveau-machine (BCI pour Brain-Computer Interfaces). Ce type d'interfaces s'est développé dans les années récentes tout d'abord dans le domaine du handicap, afin de fournir aux grands handicapés des moyens d'interaction et de communication, et plus récemment dans d'autres domaines comme celui des jeux vidéo. Néanmoins, la plupart des travaux ont porté sur l'identification des signaux du cerveau susceptibles de porter une information utile, et sur les traitements nécessaires à l'extraction de cette information. Peu de travaux ont porté sur les aspects d'utilisabilité et de prise en compte des facteurs humains dans l'ensemble du système interactif. Cette thèse se concentre sur les systèmes basées sur SSVEP (steady-state visually evoked potentials), et se propose d'étudier l'ensemble du système interactif cerveau-machine, selon les critères de l'interaction homme-machine (IHM). Plus précisément, les points étudiés portent sur la demande cognitive, la frustration de l'utilisateur, les conditions de calibration, et les BCI hybrides. / This PhD deals with the conception and evaluation of interactive systems based on Brain-Computer Interfaces (BCI). This type of interfaces has developed in recent years, first in the domain of handicaps, in order to provide disabled people means of interaction and communication, and more recently in other fields as video games. However, most of the research so far focused on the identification of cerebral pattern carrying useful information, a on signal processing for the detection of these patterns. Less attention has been given to usability aspects. This PhD focuses on interactive systems based on Steady-State Visually Evoked Potentials (SSVEP), and aims at considering the interactive system as a whole, using the concepts of Human-Computer Interaction. More precisely, a focus is made on cognitive demand, user frustration, calibration conditions, and hybrid BCIs.

Comment le sens est-il extrait de l'information visuelle ? Cette thèse est focalisée sur la capacité du système visuel d'humains et de singes à extraire et représenter l'information visuelle sur différents niveaux de complexité. Nous avons étudié différent niveaux de représentations visuelles, de la production de représentations visuelles primaires jusqu'à l'élaboration de représentations visuelles conscientes. Ce manuscrit présente six travaux dans lesquels nous avons exploré : (1) les attributs visuels nécessaires pour réaliser la tâche de catégorisation ultra-rapide chez l'homme et le singe au moyen de méthodes psychophysiques, (2) la dynamique spatio-temporelle de l'attention visuelle chez l'homme au moyen de méthodes psychophysiques, (3) les corrélats neuronaux des représentations de haut niveau en EEG grâce au développement d'une nouvelle technique appelée SWIFT, (4) les corrélats neuronaux de la conscience visuelle dans la rivalité binoculaire en EEG, (5) la synchronie des signaux cérébraux en fonction de la reconnaissance consciente au moyen d'enregistrements intracrâniens chez des patients épileptiques et (6) les corrélats neuronaux associés à la prise de conscience chez le singe au moyen d'enregistrements intracrâniens. Les résultats de ces travaux nous ont permis d'ébaucher un modèle de la perception visuelle, cherchant à dissocier l'attention et la conscience.

A brain-machine interface (BMI) is a system that allows the communication between the central nervous system (CNS) and an external device (Wolpaw et al. 2002). Applications of BMIs include the control of external prostheses, cursors and spellers, to name a few. The BMIs developed by various research groups differ in their characteristics (e.g. continuous or discrete, synchronous or asynchronous, degrees of freedom, others) and, in spite of several initiatives towards standardization and guidelines, the cross comparison across studies remains a challenge (Brunner et al. 2015; Thompson et al. 2014). Here, we used a 64-channel EEG equipment to acquire data from 19 healthy participants during three different tasks (SSVEP, P300 and hybrid) that allowed four choices to the user and required no previous neurofeedback training. We systematically compared the offline performance of the three tasks on the following parameters: a) accuracy, b) information transfer rate, c) illiteracy/inefficiency, and d) individual preferences. Additionally, we selected the best performing channels per task and evaluated the accuracy as a function of the number of electrodes. Our results demonstrate that the SSVEP task outperforms the other tasks in accuracy, ITR and illiteracy/inefficiency, reaching an average ITR** of 52,8 bits/min and a maximum ITR** of 104,2 bits/min. Additionally, all participants achieved an accuracy level above 70% (illiteracy/inefficiency threshold) in both SSVEP and P300 tasks. Furthermore, the average accuracy of all tasks did not deteriorate if a reduced set with only the 8 best performing electrodes were used. These results are relevant for the development of online BMIs, including aspects related to usability, user satisfaction and portability. / A interface cérebro-máquina (ICM) é um sistema que permite a comunicação entre o sistema nervoso central e um dispositivo externo (Wolpaw et al., 2002). Aplicações de ICMs incluem o controle de próteses externa, cursores e teclados virtuais, para citar alguns. As ICMs desenvolvidas por vários grupos de pesquisa diferem em suas características (por exemplo, contínua ou discreta, síncrona ou assíncrona, graus de liberdade, outras) e, apesar de várias iniciativas voltadas para diretrizes de padronização, a comparação entre os estudos continua desafiadora (Brunner et al. 2015, Thompson et al., 2014). Aqui, utilizamos um equipamento EEG de 64 canais para adquirir dados de 19 participantes saudáveis ao longo da execução de três diferentes tarefas (SSVEP, P300 e híbrida) que permitiram quatro escolhas ao usuário e não exigiram nenhum treinamento prévio. Comparamos sistematicamente o desempenho \"off-line\" das três tarefas nos seguintes parâmetros: a) acurácia, b) taxa de transferência de informação, c) analfabetismo / ineficiência e d) preferências individuais. Além disso, selecionamos os melhores canais por tarefa e avaliamos a acurácia em função do número de eletrodos. Nossos resultados demonstraram que a tarefa SSVEP superou as demais em acurácia, ITR e analfabetismo/ineficiência, atingindo um ITR** médio de 52,8 bits/min e um ITR** máximo de 104,2 bits/min. Adicionalmente, todos os participantes alcançaram um nível de acurácia acima de 70% (limiar de analfabetismo/ineficiência) nas tarefas SSVEP e P300. Além disso, a acurácia média de todas as tarefas não se deteriorou ao se utilizar um conjunto reduzido composto apenas pelos melhores 8 eletrodos. Estes resultados são relevantes para o desenvolvimento de ICMs \"online\", incluindo aspectos relacionados à usabilidade, satisfação do usuário e portabilidade.

A brain-machine interface (BMI) is a system that allows the communication between the central nervous system (CNS) and an external device (Wolpaw et al. 2002). Applications of BMIs include the control of external prostheses, cursors and spellers, to name a few. The BMIs developed by various research groups differ in their characteristics (e.g. continuous or discrete, synchronous or asynchronous, degrees of freedom, others) and, in spite of several initiatives towards standardization and guidelines, the cross comparison across studies remains a challenge (Brunner et al. 2015; Thompson et al. 2014). Here, we used a 64-channel EEG equipment to acquire data from 19 healthy participants during three different tasks (SSVEP, P300 and hybrid) that allowed four choices to the user and required no previous neurofeedback training. We systematically compared the offline performance of the three tasks on the following parameters: a) accuracy, b) information transfer rate, c) illiteracy/inefficiency, and d) individual preferences. Additionally, we selected the best performing channels per task and evaluated the accuracy as a function of the number of electrodes. Our results demonstrate that the SSVEP task outperforms the other tasks in accuracy, ITR and illiteracy/inefficiency, reaching an average ITR** of 52,8 bits/min and a maximum ITR** of 104,2 bits/min. Additionally, all participants achieved an accuracy level above 70% (illiteracy/inefficiency threshold) in both SSVEP and P300 tasks. Furthermore, the average accuracy of all tasks did not deteriorate if a reduced set with only the 8 best performing electrodes were used. These results are relevant for the development of online BMIs, including aspects related to usability, user satisfaction and portability. / A interface cérebro-máquina (ICM) é um sistema que permite a comunicação entre o sistema nervoso central e um dispositivo externo (Wolpaw et al., 2002). Aplicações de ICMs incluem o controle de próteses externa, cursores e teclados virtuais, para citar alguns. As ICMs desenvolvidas por vários grupos de pesquisa diferem em suas características (por exemplo, contínua ou discreta, síncrona ou assíncrona, graus de liberdade, outras) e, apesar de várias iniciativas voltadas para diretrizes de padronização, a comparação entre os estudos continua desafiadora (Brunner et al. 2015, Thompson et al., 2014). Aqui, utilizamos um equipamento EEG de 64 canais para adquirir dados de 19 participantes saudáveis ao longo da execução de três diferentes tarefas (SSVEP, P300 e híbrida) que permitiram quatro escolhas ao usuário e não exigiram nenhum treinamento prévio. Comparamos sistematicamente o desempenho \"off-line\" das três tarefas nos seguintes parâmetros: a) acurácia, b) taxa de transferência de informação, c) analfabetismo / ineficiência e d) preferências individuais. Além disso, selecionamos os melhores canais por tarefa e avaliamos a acurácia em função do número de eletrodos. Nossos resultados demonstraram que a tarefa SSVEP superou as demais em acurácia, ITR e analfabetismo/ineficiência, atingindo um ITR** médio de 52,8 bits/min e um ITR** máximo de 104,2 bits/min. Adicionalmente, todos os participantes alcançaram um nível de acurácia acima de 70% (limiar de analfabetismo/ineficiência) nas tarefas SSVEP e P300. Além disso, a acurácia média de todas as tarefas não se deteriorou ao se utilizar um conjunto reduzido composto apenas pelos melhores 8 eletrodos. Estes resultados são relevantes para o desenvolvimento de ICMs \"online\", incluindo aspectos relacionados à usabilidade, satisfação do usuário e portabilidade.

Brain-computer interface (BCI) or Brain Machine Interface (BMI) provides direct communication channel between user’s brain and an external device without any requirement of user’s physical movement. Primarily BCI has been employed in medical sciences to facilitate the patients with severe motor, visual and aural impairments. More recently many BCI are also being used as a part of entertainment. BCI differs from Neuroprosthetics, a study within Neuroscience, in terms of its usage; former connects the brain with a computer or external device while the later connects the nervous system to an implanted device. A BCI receives the modulated input from user either invasively or non-invasively. The modulated input, concealed in the huge amount of noise, contains distinct brain patterns based on the type of activity user is performing at that point in time. Primary task of a typical BCI is to find out those distinct brain patterns and translates them to meaningful communication command set. Cursor controllers, Spellers, Wheel Chair and robot Controllers are classic examples of BCI applications. This study aims to investigate an Electroencephalography (EEG) based non-invasive BCI in general and its interaction with a web interface in particular. Different aspects related to BCI are covered in this work including feedback techniques, BCI frameworks, commercial BCI hardware, and different BCI applications. BCI paradigm Steady State Visually Evoked Potentials (SSVEP) is being focused during this study. A hybrid solution is developed during this study, employing a general purpose BCI framework OpenViBE, which comprised of a low-level stimulus management and control module and a web based Google Street View client application. This study shows that a BCI can not only provide a way of communication for the impaired subjects but it can also be a multipurpose tool for a healthy person. During this study, it is being established that the major hurdles that hamper the performance of a BCI system are training protocols, BCI hardware and signal processing techniques. It is also observed that a controlled environment and expert assistance is required to operate a BCI system.

Studying newborns in the first days of life prior to experiencing the world provides remarkable insights into the neurocognitive predispositions that humans are endowed with. First, it helps us to improve our current knowledge of the development of a typical brain. Secondly, it potentially opens new pathways for earlier diagnosis of several developmental neurocognitive disorders such as Autism Spectrum Disorder (ASD). While most studies investigating early cognition in the literature are purely behavioural, recently there has been an increasing number of neuroimaging studies in newborns and infants. Electroencephalography (EEG) is one of the most optimal neuroimaging technique to investigate neurocognitive functions in human newborns because it is non-invasive and quick and easy to mount on the head. Since EEG offers a versatile design with custom number of channels/electrodes, an ergonomic wearable solution could help study newborns outside clinical settings such as their homes. Compared to adult EEG, newborn EEG data are different in two main aspects: 1) In experimental designs investigating stimulus-related neural responses, collected data is extremely short in length due to the reduced attentional span of newborns; 2) Data is heavily contaminated with noise due to their uncontrollable movement artifacts. Since EEG processing methods for adults are not adapted to very short data length and usually deal with well-defined, stereotyped artifacts, they are unsuitable for newborn EEG. As a result, researchers manually clean the data, which is a subjective and time-consuming task. This thesis work is specifically dedicated to developing (semi-) automated novel signal processing methods for noise removal and for extracting reliable neural responses specific to this population. The solutions are proposed for both high-density EEG for traditional lab-based research and wearable EEG for clinical applications. To this end, this thesis, first, presents novel signal processing methods applied to newborn EEG: 1) Local Outlier Factor (LOF) for detecting and removing bad/noisy channels; 2) Artifacts Subspace Reconstruction (ASR) for detecting and removing or correcting bad/noisy segments. Then, based on these algorithms and other preprocessing functionalities, a robust preprocessing pipeline, Newborn EEG Artifact Removal (NEAR), is proposed. Notably, this is the first time LOF is explored for EEG bad channel detection, despite being a popular outlier detection technique in other kinds of data such as Electrocardiogram (ECG). Even if ASR is already an established artifact real algorithm originally developed for mobile adult EEG, this thesis explores the possibility of adapting ASR for short newborn EEG data, which is the first of its kind. NEAR is validated on simulated, real newborn, and infant EEG datasets. We used the SEREEGA toolbox to simulate neurologically plausible synthetic data and contaminated a certain number of channels and segments with artifacts commonly manifested in developmental EEG. We used newborn EEG data (n = 10, age range: 1 and 4 days) recorded in our lab based on a frequency-tagging paradigm. The chosen paradigm consists of visual stimuli to investigate the cortical bases of facelike pattern processing, and the results were published in 2019. To test NEAR performance on an older population with an event-related design (ERP) and with data recorded in another lab, we also evaluated NEAR on infant EEG data recorded on 9-months-old infants (n = 14) with an ERP paradigm. The experimental paradigm for these datasets consists of auditory stimulus to investigate the electrophysiological evidence for understanding maternal speech, and the results were published in 2012. Since authors of these independent studies employed manual artifact removal, the obtained neural responses serve as ground truth for validating NEAR’s artifact removal performance. For comparative evaluation, we considered the performance of two state-of-the-art pipelines designed for older infants. Results show that NEAR is successful in recovering the neural responses (specific to the EEG paradigm and the stimuli) compared to the other pipelines. In sum, this thesis presents a set of methods for artifact removal and extraction of stimulus-related neural responses specifically adapted to newborn and infant EEG data that will hopefully contribute to strengthening the reliability and reproducibility of developmental cognitive neuroscience studies, both in research laboratories and in clinical applications.