Brain Computer Interfaces for inclusion

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An SSVEP Brain-Computer Interface: A Machine Learning Approach

A Brain-Computer Interface (BCI) provides a bidirectional communication path for a human to control an external device using brain signals. Among neurophysiological features in BCI systems, steady state visually evoked potentials (SSVEP), natural responses to visual stimulation at specific frequencies, has increasingly drawn attentions because of its high temporal resolution and minimal user training, which are two important parameters in evaluating a BCI system. The performance of a BCI can be improved by a properly selected neurophysiological signal, or by the introduction of machine learning techniques. With the help of machine learning methods, a BCI system can adapt to the user automatically. In this work, a machine learning approach is introduced to the design of an SSVEP based BCI. The following open problems have been explored: 1. Finding a waveform with high success rate of eliciting SSVEP. SSVEP belongs to the evoked potentials, which require stimulations. By comparing square wave, triangle wave and sine wave light signals and their corresponding SSVEP, it was observed that square waves with 50% duty cycle have a significantly higher success rate of eliciting SSVEPs than either sine or triangle stimuli. 2. The resolution of dual stimuli that elicits consistent SSVEP. Previous studies show that the frequency bandwidth of an SSVEP stimulus is limited. Hence it affects the performance of the whole system. A dual-stimulus, the overlay of two distinctive single frequency stimuli, can potentially expand the number of valid SSVEP stimuli. However, the improvement depends on the resolution of the dual stimuli. Our experimental results shothat 4 Hz is the minimum difference between two frequencies in a dual-stimulus that elicits consistent SSVEP. 3. Stimuli and color-space decomposition. It is known in the literature that although low-frequency stimuli (\u3c30 Hz) elicit strong SSVEP, they may cause dizziness. In this work, we explored the design of a visually friendly stimulus from the perspective of color-space decomposition. In particular, a stimulus was designed with a fixed luminance component and variations in the other two dimensions in the HSL (Hue, Saturation, Luminance) color-space. Our results shothat the change of color alone evokes SSVEP, and the embedded frequencies in stimuli affect the harmonics. Also, subjects claimed that a fixed luminance eases the feeling of dizziness caused by low frequency flashing objects. 4. Machine learning techniques have been applied to make a BCI adaptive to individuals. An SSVEP-based BCI brings new requirements to machine learning. Because of the non-stationarity of the brain signal, a classifier should adapt to the time-varying statistical characters of a single user\u27s brain wave in realtime. In this work, the potential function classifier is proposed to address this requirement, and achieves 38.2bits/min on offline EEG data

Development of a SSVEP-BCI system for decision-making assistance

Orientadores: Gilmar Barreto, Linnyer Beatrys Ruiz AylonTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Nos últimos anos, Interfaces Cérebro-Computador (BCI) passaram a ter um maior foco em problemas fora do escopo clínico. Sistema BCI podem ser utilizados para controlar equipamentos elétricos e eletrônicos, controle de jogos digitais, etc. A capacidade de poder "controlar" em um sistema BCI, pode ser adaptada a uma ação que auxilia um indivíduo em tomada de decisões, por exemplo, decidir se paramos ou continuamos a conduzir um automóvel ao visualizar os estados de um semáforo de trânsito. O BCI baseado no paradigma de Potenciais Evocados Visualmente em Regime Estacionário (SSVEP), pode ser utilizado para diferenciar alvos com diferentes frequências de cintilação por meio de estímulos visuais. Esta tese de doutorado teve como objetivo avaliar o estímulo SSVEP de altas e baixas frequências admitas pelo paradigma, para a construção de um sistema SSVEP-BCI para auxiliar na tomada de decisões. Para cumprir com este objetivo, foram realizados (1) experimentos com uma base de dados pública com estímulos SSVEP armazenados, para avaliar os códigos desenvolvidos, (2) construção de uma base de dados gerada por meio de experimentos realizados com um protótipo de semáforo de trânsito, para avaliar o funcionamento do protótipo e do equipamento de eletroencefalografia (EEG) e, por fim, (3) experimentos realizados com quatro participantes para avaliar os estímulos SSVEP em baixas frequências de cintilação, tradicionalmente utilizadas do paradigma e altas frequências de cintilação configuradas em um limiar não visível aos nossos olhos, permitindo que o protótipo se comporte de forma mais próxima a situações reais e ainda forneça uma menor fadiga visual. Os resultados obtidos forneceram a exatidão dos programas desenvolvidos para avaliar os estímulos SSVEP e também o funcionamento do protótipo e do equipamento de EEG. Além disso, os experimentos realizados com os quatro participantes apresentaram em média uma acurácia de 89,37%±8,26% para baixas frequências e 80,62%±7,18% para altas frequências, no qual concluímos que o sistema SSVEP-BCI pode ser utilizado para auxiliar em situações de tomada de decisão em ambas as faixas de frequênciaAbstract: In recent years, Brain-Computer Interfaces (BCI) have an increased focus on problems outside the clinical scope. BCI system can be used to control electrical and electronic equipment, control of digital games, etc. The ability to "control" in a BCI system can be adapted to an action that assists an individual in decision-making, for example, deciding whether to stop or continue driving a car when viewing the states of a traffic light. The BCI paradigm based on Stead-State Visually Evoked Potentials (SSVEP) can be used to differentiate targets with different frequencies of scintillation through visual stimuli. This PhD thesis aimed to evaluate the SSVEP stimulus of high and low frequencies admitted by the paradigm, for the construction of a SSVEP-BCI system to assist in decision-making. In order to comply with this objective, we performed (1) experiments with a public database with stored SSVEP stimuli to evaluate developed codes, (2) we constructed a database generated through experiments carried out with a traffic light prototype, to evaluate the functioning of the prototype and electroencephalography (EEG) equipment, and finally (3) experiments was performed with four participants to evaluate the SSVEP stimuli at low scintillation frequencies, traditionally used in the paradigm and high scintillation frequencies configured in a threshold not visible to our eyes, allowing the prototype to behave more closely to real-world situations and still provide less visual fatigue. The results obtained provided the correct execution of written programs to evaluate the SSVEP stimuli and also the functioning of the prototype and the EEG equipment. In addition, the results from the experiments carried out with the four participants presented on average an accuracy of 89.37%±8.26% for low frequencies and 80.62%±7.18% for high frequencies, in which we concluded that the SSVEP-BCI system can be used to assist in decision-making situations in both frequency bandsDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

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

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