69 research outputs found
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
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
A brain-computer interface integrated with virtual reality and robotic exoskeletons for enhanced visual and kinaesthetic stimuli
Brain-computer interfaces (BCI) allow the direct control of robotic devices for neurorehabilitation and measure brain activity patterns following the user’s intent. In the past two decades, the use of non-invasive techniques such as electroencephalography and motor imagery in BCI has gained traction. However, many of the mechanisms that drive the proficiency of humans in eliciting discernible signals for BCI remains unestablished. The main objective of this thesis is to explore and assess what improvements can be made for an integrated BCI-robotic system for hand rehabilitation. Chapter 2 presents a systematic review of BCI-hand robot systems developed from 2010 to late 2019 in terms of their technical and clinical reports. Around 30 studies were identified as eligible for review and among these, 19 were still in their prototype or pre-clinical stages of development. A degree of inferiority was observed from these systems in providing the necessary visual and kinaesthetic stimuli during motor imagery BCI training. Chapter 3 discusses the theoretical background to arrive at a hypothesis that an enhanced visual and kinaesthetic stimulus, through a virtual reality (VR) game environment and a robotic hand exoskeleton, will improve motor imagery BCI performance in terms of online classification accuracy, class prediction probabilities, and electroencephalography signals. Chapters 4 and 5 focus on designing, developing, integrating, and testing a BCI-VR-robot prototype to address the research aims. Chapter 6 tests the hypothesis by performing a motor imagery BCI paradigm self-experiment with an enhanced visual and kinaesthetic stimulus against a control. A significant increase (p = 0.0422) in classification accuracies is reported among groups with enhanced visual stimulus through VR versus those without. Six out of eight sessions among the VR groups have a median of class probability values exceeding a pre-set threshold value of 0.6. Finally, the thesis concludes in Chapter 7 with a general discussion on how these findings could suggest the role of new and emerging technologies such as VR and robotics in advancing BCI-robotic systems and how the contributions of this work may help improve the usability and accessibility of such systems, not only in rehabilitation but also in skills learning and education
A comprehensive review of endogenous EEG-based BCIs for dynamic device control
Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel
approach for controlling external devices. BCI technologies can be important enabling technologies for
people with severe mobility impairment. Endogenous paradigms, which depend on user-generated
commands and do not need external stimuli, can provide intuitive control of external devices. This
paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile
robots, and robotic arms. These technologies must be able to navigate complex environments
or execute fine motor movements. Brain control of these devices presents an intricate research
problem that merges signal processing and classification techniques with control theory. In particular,
obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder
output signals can be unstable. These issues present myriad research questions that are discussed
in this review paper. This review covers papers published until the end of 2021 that presented
BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control,
stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user
experience. The paper concludes with a discussion of open questions and avenues for future work.peer-reviewe
Electroencephalography (EEG)-based Brain-Computer Interfaces
International audienceBrain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). In this article, we aim at providing an accessible and up-to-date overview of EEG-based BCI, with a main focus on its engineering aspects. We notably introduce some basic neuroscience background, and explain how to design an EEG-based BCI, in particular reviewing which signal processing, machine learning, software and hardware tools to use. We present Brain Computer Interface applications, highlight some limitations of current systems and suggest some perspectives for the field
A transfer learning algorithm to reduce brain-computer interface calibration time for long-term users
Current motor imagery-based brain-computer interface (BCI) systems require a long calibration time at the beginning of each session before they can be used with adequate levels of classification accuracy. In particular, this issue can be a significant burden for long term BCI users. This article proposes a novel transfer learning algorithm, called r-KLwDSA, to reduce the BCI calibration time for long-term users. The proposed r-KLwDSA algorithm aligns the user's EEG data collected in previous sessions to the few EEG trials collected in the current session, using a novel linear alignment method. Thereafter, the aligned EEG trials from the previous sessions and the few EEG trials from the current sessions are fused through a weighting mechanism before they are used for calibrating the BCI model. To validate the proposed algorithm, a large dataset containing the EEG data from 11 stroke patients, each performing 18 BCI sessions, was used. The proposed framework demonstrated a significant improvement in the classification accuracy, of over 4% compared to the session-specific algorithm, when there were as few as two trials per class available from the current session. The proposed algorithm was particularly successful in improving the BCI accuracy of the sessions that had initial session-specific accuracy below 60%, with an average improvement of around 10% in the accuracy, leading to more stroke patients having meaningful BCI rehabilitation
Методы классификации ЭЭГ-паттернов воображаемых движений
Рассматриваются наиболее перспективные методы классификации электроэнцефалографических сигналов при разработке неинвазивных интерфейсов мозг–компьютер и теоретических подходов для успешной классификации электроэнцефалографических паттернов. Приводится обзор работ, использующих для классификации риманову геометрию, методы глубокого обучения и различные варианты предобработки и кластеризации электроэнцефалографических сигналов, например общего пространственного фильтра. Среди прочих подходов предобработка электроэнцефалографических сигналов с применением общего пространственного фильтра часто используется как в офлайн, так и в онлайн режимах. Согласно исследованиям последних лет сочетание общего пространственного фильтра, линейного дискриминантного анализа, метода опорных векторов и нейронной сети с обратным распространением ошибки позволило достигнуть 91% точности при двухклассовой классификации с обратной связью в виде управления экзоскелетом. Исследований по использованию римановой геометрии в условиях онлайн очень мало, и на данный момент наилучшая точность при двухклассовой классификации составляет 69,3%. При этом в офлайн тестировании средний процент классификации в рассмотренных статьях для подходов с применением общего пространственного фильтра – 77,5±5,8%, сетей глубокого обучения – 81,7±4,7%, римановой геометрии – 90,2±6,6%. За счет нелинейных преобразований методы, основанные на римановой геометрии, а также на применении глубоких нейронных сетей сложной архитектуры, обеспечивают большую точность и способность к извлечению полезной информации из сигнала по сравнению с линейным преобразованием общего пространственного фильтра. Однако в условиях реального времени важна не только точность, но и минимальная временная задержка. Здесь преимущество может быть за подходами с использованием преобразования общего пространственного фильтра и римановой геометрии с временной задержкой менее 500 мс
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