40 research outputs found
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
Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis
The use of Brain-Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain's capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training.info:eu-repo/semantics/publishedVersio
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
Comparison of visual and auditory modalities for Upper-Alpha EEG-Neurofeedback
Electroencephalography (EEG) neurofeedback
(NF) training has been shown to produce long-lasting effects on
the improvement of cognitive function as well as the
normalization of aberrant brain activity in disease. However,
the impact of the sensory modality used as the NF
reinforcement signal on training effectiveness has not been
systematically investigated. In this work, an EEG-based NF training system was developed targeting the individual upper alpha (UA) band and using either a visual or an auditory
reinforcement signal, so as to compare the effects of the two
sensory modalities. Sixteen healthy volunteers were randomly
assigned to the Visual or Auditory group, where a radius varying sphere or a volume-varying sound, respectively,
reflected the relative amplitude of UA measured at EEG
electrode Cz. Each participant underwent a total of four NF
sessions, of approximately 40 min each, on consecutive days.
Both groups showed significant increases in UA at Cz within
sessions, and also across sessions. Effects subsequent to NF
training were also found beyond the target frequency UA and
scalp location Cz, namely in the lower-alpha and theta bands
and in posterior brain regions, respectively. Only small
differences were found on the EEG between the Visual and
Auditory groups, suggesting that auditory reinforcement
signals may be as effective as the more commonly used visual
signals. The use of auditory NF may potentiate training
protocols conducted under mobile conditions, which are now
possible due to the increasing availability of wireless EEG
systems.info:eu-repo/semantics/publishedVersio
New Approaches Based on Non-Invasive Brain Stimulation and Mental Representation Techniques Targeting Pain in Parkinson’s Disease Patients: Two Study Protocols for Two Randomized Controlled Trials.
Pain is an under-reported but prevalent symptom in Parkinson’s Disease (PD), impacting patients’ quality of life. Both pain and PD conditions cause cortical excitability reduction and non-invasive brain stimulation. Mental representation techniques are thought to be able to counteract it, also resulting effectively in chronic pain conditions. We aim to conduct two independent studies in order to evaluate the efficacy of transcranial direct current stimulation (tDCS) and mental representation protocol in the management of pain in PD patients during the ON state: (1) tDCS over the Primary Motor Cortex (M1); and (2) Action Observation (AO) and Motor Imagery (MI) training through a Brain-Computer Interface (BCI) using Virtual Reality (AO + MI-BCI). Both studies will include 32 subjects in a longitudinal prospective parallel randomized controlled trial design under different blinding conditions. The main outcomes will be score changes in King’s Parkinson’s Disease Pain Scale, Brief Pain Inventory, Temporal Summation, Conditioned Pain Modulation, and Pain Pressure Threshold. Assessment will be performed pre-intervention, post-intervention, and 15 days post-intervention, in both ON and OFF states.post-print453 K
Clinical Effects of Immersive Multimodal BCI-VR Training after Bilateral Neuromodulation with rTMS on Upper Limb Motor Recovery after Stroke. A Study Protocol for a Randomized Controlled Trial.
Background and Objectives: The motor sequelae after a stroke are frequently persistent and
cause a high degree of disability. Cortical ischemic or hemorrhagic strokes affecting the corticospinal
pathways are known to cause a reduction of cortical excitability in the lesioned area not only
for the local connectivity impairment but also due to a contralateral hemisphere inhibitory action.
Non-invasive brain stimulation using high frequency repetitive magnetic transcranial stimulation
(rTMS) over the lesioned hemisphere and contralateral cortical inhibition using low-frequency rTMS
have been shown to increase the excitability of the lesioned hemisphere. Mental representation
techniques, neurofeedback, and virtual reality have also been shown to increase cortical excitability
and complement conventional rehabilitation. Materials and Methods: We aim to carry out a single-blind,
randomized, controlled trial aiming to study the efficacy of immersive multimodal Brain–Computer
Interfacing-Virtual Reality (BCI-VR) training after bilateral neuromodulation with rTMS on upper
limb motor recovery after subacute stroke (>3 months) compared to neuromodulation combined with
conventional motor imagery tasks. This study will include 42 subjects in a randomized controlled
trial design. The main expected outcomes are changes in the Motricity Index of the Arm (MI),
dynamometry of the upper limb, score according to Fugl-Meyer for upper limb (FMA-UE), and
changes in the Stroke Impact Scale (SIS). The evaluation will be carried out before the intervention,
after each intervention and 15 days after the last session. Conclusions: This trial will show the additive
value of VR immersive motor imagery as an adjuvant therapy combined with a known effective
neuromodulation approach opening new perspectives for clinical rehabilitation protocols.post-print966 K
Investigating the impact of visual perspective in a motor imagery-based brain-robot interaction: A pilot study with healthy participants
IntroductionMotor Imagery (MI)-based Brain Computer Interfaces (BCI) have raised gained attention for their use in rehabilitation therapies since they allow controlling an external device by using brain activity, in this way promoting brain plasticity mechanisms that could lead to motor recovery. Specifically, rehabilitation robotics can provide precision and consistency for movement exercises, while embodied robotics could provide sensory feedback that can help patients improve their motor skills and coordination. However, it is still not clear whether different types of visual feedback may affect the elicited brain response and hence the effectiveness of MI-BCI for rehabilitation.MethodsIn this paper, we compare two visual feedback strategies based on controlling the movement of robotic arms through a MI-BCI system: 1) first-person perspective, with visual information that the user receives when they view the robot arms from their own perspective; and 2) third-person perspective, whereby the subjects observe the robot from an external perspective. We studied 10 healthy subjects over three consecutive sessions. The electroencephalographic (EEG) signals were recorded and evaluated in terms of the power of the sensorimotor rhythms, as well as their lateralization, and spatial distribution.ResultsOur results show that both feedback perspectives can elicit motor-related brain responses, but without any significant differences between them. Moreover, the evoked responses remained consistent across all sessions, showing no significant differences between the first and the last session.DiscussionOverall, these results suggest that the type of perspective may not influence the brain responses during a MI- BCI task based on a robotic feedback, although, due to the limited sample size, more evidence is required. Finally, this study resulted into the production of 180 labeled MI EEG datasets, publicly available for research purposes
A user-centred approach to unlock the potential of non-invasive BCIs: an unprecedented international translational effort
Non-invasive Mental Task-based Brain-Computer Interfaces (MT-BCIs) enable their users to interact with the environment through their brain activity alone (measured using electroencephalography for example), by performing mental tasks such as mental calculation or motor imagery. Current developments in technology hint at a wide range of possible applications, both in the clinical and non-clinical domains. MT-BCIs can be used to control (neuro)prostheses or interact with video games, among many other applications. They can also be used to restore cognitive and motor abilities for stroke rehabilitation, or even improve athletic performance.Nonetheless, the expected transfer of MT-BCIs from the lab to the marketplace will be greatly impeded if all resources are allocated to technological aspects alone. We cannot neglect the Human End-User that sits in the centre of the loop. Indeed, self-regulating one’s brain activity through mental tasks to interact is an acquired skill that requires appropriate training. Yet several studies have shown that current training procedures do not enable MT-BCI users to reach adequate levels of performance. Therefore, one significant challenge for the community is that of improving end-user training.To do so, another fundamental challenge must be taken into account: we need to understand the processes that underlie MT-BCI performance and user learning. It is currently estimated that 10 to 30% of people cannot control an MT-BCI. These people are often referred to as “BCI inefficient”. But the concept of “BCI inefficiency” is debated. Does it really exist? Or, are low performances due to insufficient training, training procedures that are unsuited to these users or is the BCI data processing not sensitive enough? The currently available literature does not allow for a definitive answer to these questions as most published studies either include a limited number of participants (i.e., 10 to 20 participants) and/or training sessions (i.e., 1 or 2). We still have very little insight into what the MT-BCI learning curve looks like, and into which factors (including both user-related and machine-related factors) influence this learning curve. Finding answers will require a large number of experiments, involving a large number of participants taking part in multiple training sessions. It is not feasible for one research lab or even a small consortium to undertake such experiments alone. Therefore, an unprecedented coordinated effort from the research community is necessary.We are convinced that combining forces will allow us to characterise in detail MT-BCI user learning, and thereby provide a mandatory step toward transferring BCIs “out of the lab”. This is why we gathered an international, interdisciplinary consortium of BCI researchers from more than 20 different labs across Europe and Japan, including pioneers in the field. This collaboration will enable us to collect considerable amounts of data (at least 100 participants for 20 training sessions each) and establish a large open database. Based on this precious resource, we could then lead sound analyses to answer the previously mentioned questions. Using this data, our consortium could offer solutions on how to improve MT-BCI training procedures using innovative approaches (e.g., personalisation using intelligent tutoring systems) and technologies (e.g., virtual reality). The CHIST-ERA programme represents a unique opportunity to conduct this ambitious project, which will foster innovation in our field and strengthen our community