Thought-controlled games with brain-computer interfaces

Nowadays, EEG based BCI systems are starting to gain ground in games for health research. With reduced costs and promising an innovative and exciting new interaction paradigm, attracted developers and researchers to use them on video games for serious applications. However, with researchers focusing mostly on the signal processing part, the interaction aspect of the BCIs has been neglected. A gap between classification performance and online control quality for BCI based systems has been created by this research disparity, resulting in suboptimal interactions that lead to user fatigue and loss of motivation over time. Motor-Imagery (MI) based BCIs interaction paradigms can provide an alternative way to overcome motor-related disabilities, and is being deployed in the health environment to promote the functional and structural plasticity of the brain. A BCI system in a neurorehabilitation environment, should not only have a high classification performance, but should also provoke a high level of engagement and sense of control to the user, for it to be advantageous. It should also maximize the level of control on user’s actions, while not requiring them to be subject to long training periods on each specific BCI system. This thesis has two main contributions, the Adaptive Performance Engine, a system we developed that can provide up to 20% improvement to user specific performance, and NeuRow, an immersive Virtual Reality environment for motor neurorehabilitation that consists of a closed neurofeedback interaction loop based on MI and multimodal feedback while using a state-of-the-art Head Mounted Display.Hoje em dia, os sistemas BCI baseados em EEG estão a começar a ganhar terreno em jogos relacionados com a saúde. Com custos reduzidos e prometendo um novo e inovador paradigma de interação, atraiu programadores e investigadores para usá-los em vídeo jogos para aplicações sérias. No entanto, com os investigadores focados principalmente na parte do processamento de sinal, o aspeto de interação dos BCI foi negligenciado. Um fosso entre o desempenho da classificação e a qualidade do controle on-line para sistemas baseados em BCI foi criado por esta disparidade de pesquisa, resultando em interações subótimas que levam à fadiga do usuário e à perda de motivação ao longo do tempo. Os paradigmas de interação BCI baseados em imagética motora (IM) podem fornecer uma maneira alternativa de superar incapacidades motoras, e estão sendo implementados no sector da saúde para promover plasticidade cerebral funcional e estrutural. Um sistema BCI usado num ambiente de neuro-reabilitação, para que seja vantajoso, não só deve ter um alto desempenho de classificação, mas também deve promover um elevado nível de envolvimento e sensação de controlo ao utilizador. Também deve maximizar o nível de controlo nas ações do utilizador, sem exigir que sejam submetidos a longos períodos de treino em cada sistema BCI específico. Esta tese tem duas contribuições principais, o Adaptive Performance Engine, um sistema que desenvolvemos e que pode fornecer até 20% de melhoria para o desempenho específico do usuário, e NeuRow, um ambiente imersivo de Realidade Virtual para neuro-reabilitação motora, que consiste num circuito fechado de interação de neuro-feedback baseado em IM e feedback multimodal e usando um Head Mounted Display de última geração

Using brain-computer interaction and multimodal virtual-reality for augmenting stroke neurorehabilitation

Every year millions of people suffer from stroke resulting to initial paralysis, slow motor recovery and chronic conditions that require continuous reha bilitation and therapy. The increasing socio-economical and psychological impact of stroke makes it necessary to find new approaches to minimize its sequels, as well as novel tools for effective, low cost and personalized reha bilitation. The integration of current ICT approaches and Virtual Reality (VR) training (based on exercise therapies) has shown significant improve ments. Moreover, recent studies have shown that through mental practice and neurofeedback the task performance is improved. To date, detailed in formation on which neurofeedback strategies lead to successful functional recovery is not available while very little is known about how to optimally utilize neurofeedback paradigms in stroke rehabilitation. Based on the cur rent limitations, the target of this project is to investigate and develop a novel upper-limb rehabilitation system with the use of novel ICT technolo gies including Brain-Computer Interfaces (BCI’s), and VR systems. Here, through a set of studies, we illustrate the design of the RehabNet frame work and its focus on integrative motor and cognitive therapy based on VR scenarios. Moreover, we broadened the inclusion criteria for low mobility pa tients, through the development of neurofeedback tools with the utilization of Brain-Computer Interfaces while investigating the effects of a brain-to-VR interaction.Todos os anos, milho˜es de pessoas sofrem de AVC, resultando em paral isia inicial, recupera¸ca˜o motora lenta e condic¸˜oes cr´onicas que requerem re abilita¸ca˜o e terapia cont´ınuas. O impacto socioecon´omico e psicol´ogico do AVC torna premente encontrar novas abordagens para minimizar as seque las decorrentes, bem como desenvolver ferramentas de reabilita¸ca˜o, efetivas, de baixo custo e personalizadas. A integra¸c˜ao das atuais abordagens das Tecnologias da Informa¸ca˜o e da Comunica¸ca˜o (TIC) e treino com Realidade Virtual (RV), com base em terapias por exerc´ıcios, tem mostrado melhorias significativas. Estudos recentes mostram, ainda, que a performance nas tare fas ´e melhorada atrav´es da pra´tica mental e do neurofeedback. At´e a` data, na˜o existem informac¸˜oes detalhadas sobre quais as estrat´egias de neurofeed back que levam a uma recupera¸ca˜o funcional bem-sucedida. De igual modo, pouco se sabe acerca de como utilizar, de forma otimizada, o paradigma de neurofeedback na recupera¸c˜ao de AVC. Face a tal, o objetivo deste projeto ´e investigar e desenvolver um novo sistema de reabilita¸ca˜o de membros supe riores, recorrendo ao uso de novas TIC, incluindo sistemas como a Interface C´erebro-Computador (ICC) e RV. Atrav´es de um conjunto de estudos, ilus tramos o design do framework RehabNet e o seu foco numa terapia motora e cognitiva, integrativa, baseada em cen´arios de RV. Adicionalmente, ampli amos os crit´erios de inclus˜ao para pacientes com baixa mobilidade, atrav´es do desenvolvimento de ferramentas de neurofeedback com a utilizac¸˜ao de ICC, ao mesmo que investigando os efeitos de uma interac¸˜ao c´erebro-para-RV

Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.info:eu-repo/semantics/publishedVersio

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Brain–computer interfacing with interactive systems-Case study 2

info:eu-repo/semantics/publishedVersio

Enhancement of Robot-Assisted Rehabilitation Outcomes of Post-Stroke Patients Using Movement-Related Cortical Potential

Post-stroke rehabilitation is essential for stroke survivors to help them regain independence and to improve their quality of life. Among various rehabilitation strategies, robot-assisted rehabilitation is an efficient method that is utilized more and more in clinical practice for motor recovery of post-stroke patients. However, excessive assistance from robotic devices during rehabilitation sessions can make patients perform motor training passively with minimal outcome. Towards the development of an efficient rehabilitation strategy, it is necessary to ensure the active participation of subjects during training sessions. This thesis uses the Electroencephalography (EEG) signal to extract the Movement-Related Cortical Potential (MRCP) pattern to be used as an indicator of the active engagement of stroke patients during rehabilitation training sessions. The MRCP pattern is also utilized in designing an adaptive rehabilitation training strategy that maximizes patients’ engagement. This project focuses on the hand motor recovery of post-stroke patients using the AMADEO rehabilitation device (Tyromotion GmbH, Austria). AMADEO is specifically developed for patients with fingers and hand motor deficits. The variations in brain activity are analyzed by extracting the MRCP pattern from the acquired EEG data during training sessions. Whereas, physical improvement in hand motor abilities is determined by two methods. One is clinical tests namely Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) which include FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements’ tests. The other method is the measurement of hand-kinematic parameters using the AMADEO assessment tool which contains hand strength measurements during flexion (force-flexion), and extension (force-extension), and Hand Range of Movement (HROM)

Standardization of Protocol Design for User Training in EEG-based Brain-Computer Interface

International audienceBrain-computer interfaces (BCIs) are systems that enable a personto interact with a machine using only neural activity. Such interaction canbe non-intuitive for the user hence training methods are developed to increaseone’s understanding, confidence and motivation, which would in parallel increasesystem performance. To clearly address the current issues in the BCI usertraining protocol design, here it is divided intointroductoryperiod and BCIinteractionperiod. First, theintroductoryperiod (before BCI interaction) mustbe considered as equally important as the BCI interaction for user training. Tosupport this claim, a review of papers show that BCI performance can dependon the methodologies presented in such introductory period. To standardize itsdesign, the literature from human-computer interaction (HCI) is adjusted to theBCI context. Second, during the user-BCI interaction, the interface can takea large spectrum of forms (2D, 3D, size, color etc.) and modalities (visual,auditory or haptic etc.) without following any design standard or guidelines.Namely, studies that explore perceptual affordance on neural activity show thatmotor neurons can be triggered from a simple observation of certain objects, anddepending on objects’ properties (size, location etc.) neural reactions can varygreatly. Surprisingly, the effects of perceptual affordance were not investigatedin the BCI context. Both inconsistent introductions to BCI as well as variableinterface designs make it difficult to reproduce experiments, predict their outcomesand compare results between them. To address these issues, a protocol designstandardization for user training is proposed