Vibrotactile Feedback for Brain-Computer Interface Operation

To be correctly mastered, brain-computer interfaces (BCIs) need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified. In three studies with 33 subjects (including 3 with spinal cord injury), we compared vibrotactile and visual feedback, addressing: (I) the feasibility of subjects' training to master their EEG rhythms using tactile feedback; (II) the compatibility of this form of feedback in presence of a visual distracter; (III) the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The stimulation protocol we developed supports a general usage of the tactors; preliminary experimentations. All studies indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task. In all experiments, vibrotactile feedback felt, after some training, more natural for both controls and SCI users

Vibrotactile Feedback in the Context of Mu-Rhythm based BCI

Brain-Computer Interfaces (BCIs) need an uninterrupted flow of feedback to the user, which is usually delivered through the visual channel. Our aim is to explore the benefits of vibrotactile feedback during users� training and control of EEG-based BCI applications. An experimental setup for delivery of vibrotactile feedback, including specific hardware and software arrangements, was specified. We compared vibrotactile and visual feedback, addressing the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The preliminary experimental setup included a simulated BCI control. in which all parts reflected the computational and actuation process of an actual BCI, except the souce, which was simulated using a �noisy� PC mouse. Results indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task

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

Augmenting Motor Imagery Learning for Brain–Computer Interfacing Using Electrical Stimulation as Feedback

International audienceBrain-computer Interfaces (BCI) and Functional electrical stimulation (FES) contribute significantly to induce cortical learning and to elicit peripheral neuronal activation processes and thus, are highly effective to promote motor recovery. This study aims at understanding the effect of FES as a neural feedback and its influence on the learning process for motor imagery tasks while comparing its performance with a classical visual feedback protocol. The participants were randomly separated into two groups: one group was provided with visual feedback (VIS) while the other received electrical stimulation (FES) as feedback. Both groups performed various motor imagery tasks while feedback was provided in form of a bi-directional bar for VIS group and targeted electrical stimulation on the upper and lower limbs for FES group. The results shown in this paper suggest that the FES based feedback is more intuitive to the participants, hence, the superior results as compared to the visual feedback. The results suggest that the convergence of BCI with FES modality could improve the learning of the patients both in terms of accuracy and speed and provide a practical solution to the BCI learning process in rehabilitation

Platform for analyzing multi-tasking capabilities during BCI operation

Operating brain-actuated devices requires split attention between the interaction of the device with its environment and the Brain-Computer Interface (BCI) feedback. Recently we demonstrated that BCI feedback could be provided via tactile stimulation and no difference between tactile and visual BCI feedback could be found. In this work we want to present a platform for analyzing these multi-tasking capabilities during BCI operation. Thereby, the subjects will have to perform a visual engaging and complex task in a game like setup while interacting with the BCI in a coordinated multi-tasking manner

Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance

Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable for many subjects. It has been suggested that the usage of somatosensory feedback would be more suitable than standardly used visual feedback to enhance MI brain patterns. However, somatosensory feedback should not interfere with the recorded MI brain pattern. In this study we propose a novel feedback modality to guide subjects during MI based on sensory threshold neuromuscular electrical stimulation (St-NMES). St-NMES depolarizes sensory and motor axons without eliciting any muscular contraction. We hypothesize that St-NMES does not induce detectable ERD brain patterns and fosters MI performance. Twelve novice subjects were included in a cross-over design study. We recorded their EEG, comparing St-NMES with visual feedback during MI or resting tasks. We found that St-NMES not only induced significantly larger desynchronization over sensorimotor areas (p<0.05) but also significantly enhanced MI brain connectivity patterns. Moreover, classification accuracy and stability were significantly higher with St-NMES. Importantly, St-NMES alone did not induce detectable artifacts, but rather the changes in the detected patterns were due to an increased MI performance. Our findings indicate that St-NMES is a promising feedback in order to foster MI performance and cold be used for BMI online applications

Apprentissage humain pour les interfaces cerveau-ordinateur

International audienceNous avons réalisé une revue de la littérature au sujet des protocoles d’entraînement utilisateur actuels pour l’apprentissage des MIBCI. Nous nous sommes plus précisément concentrés sur le protocole proposé par l’équipe BCI de Graz et sur ses variantes. La comparaison de ce protocole avec les recommandations issues de la psychologie et de l’ingénierie pédagogique nous a permis d’établir les limitations des procédures d’entraînement actuelles et de proposer des lignes directrices pour le design de futurs protocols. Notamment, nous insistons sur le fait que l’on devrait fournir aux apprenants des instructions qui spécifient de manière explicite l’objectif de l’entraînement ; que les tâches d’entraînement devraient être adaptives et proposer une augmentation de la difficulté progressive ; que le feedback devrait être multi-modal, explicatif et supportif ; et que l’environnement d’apprentissage devrait être motivant

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

Brain Computer Interfaces: Challenges to Clinical Viability Addressed in the Laboratory

Paralysis following spinal cord injuries, amyotrophic lateral sclerosis, stroke, and other disorders can intervene with signal transduction from the brain to the motor periphery, and eliminate the ability to perform volitional movements. Brain computer interfaces (BCI) directly measure brain activity associated with the user’s intent and translate the recorded brain activity into control signals for BCI applications, such as moving a computer cursor or a robot arm. While BCI technology has become an active and exciting field of research, much of the field’s development and achievements to date have taken place in the laboratory. The translation of BCI technology to the clinical setting is still not a reality. My thesis research has been dedicated to the objective of facilitating the translation of BCI systems from the primate lab to a clinical setting. That guiding objective has led me to work on several projects including: a technique that vastly improves the longevity of surgical implants in primate studies; a task that pushes the limits of sensorimotor performance – improving our knowledge of the function of primary motor cortex during realistic reaches and allowing us to quantify feedback effectiveness; characterizing the long-term tissue response to chronically implanted electrodes, and investigating how to optimally select parameters for neural information extraction. Each of these contributions will help bring BCI systems one step closer to clinical reality