1,720,996 research outputs found
EEG correlates of video game experience and user profile in motor-imagery-based brain–computer interaction
No full textThrough the use of brain–computer interfaces (BCIs), neurogames have become increasingly more advanced by incorporating immersive virtual environments and 3D worlds. However, training both the user and the systemrequireslongandrepetitivetrialsresultinginfatigueand lowperformance.Moreover,manyusersareunabletovoluntarilymodulatetheamplitudeoftheirbrainactivitytocontrol theneurofeedbackloop.Inthisstudy,wearefocusingonthe effect that gaming experience has in brain activity modulation as an attempt to systematically identify the elements that contribute to high BCI control and to be utilized in neurogamedesign.Basedonthecurrentliterature,wearguethat experienced gamers could have better performance in BCI trainingduetoenhancedsensorimotorlearningderivedfrom gaming. To investigate this, two experimental studies were conducted with 20 participants overall, undergoing 3 BCI sessions,resultingin88EEGdatasets.Resultsindicate(a)an effectfrombothdemographicandgamingexperiencedatato theactivitypatternsofEEGrhythms,and(b)increasedgamingexperiencemightnotincreasesignificantlyperformance, but it could provide faster learning for ‘Hardcore’ gamers.info:eu-repo/semantics/publishedVersio
Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis
Full text linkThe 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
NeuRow: an immersive VR environment for motor-imagery training with the use of brain-computer interfaces and vibrotactile feedback
No full textMotor-Imagery offers a solid foundation for the development of Brain-Computer Interfaces (BCIs), capable of direct brain-to-computer communication but also effective in alleviating neurological impairments. The fusion of BCIs with Virtual Reality (VR) allowed the enhancement of the field of virtual rehabilitation by including patients with low-level of motor control with limited access to treatment. BCI-VR technology has pushed research towards finding new solutions for better and reliable BCI control. Based on our previous work, we have developed NeuRow, a novel multiplatform prototype that makes use of multimodal feedback in an immersive VR environment delivered through a state-of-the-art Head Mounted Display (HMD). In this article we present the system design and development, including important features for creating a closed neurofeedback loop in an implicit manner, and preliminary data on user performance and user acceptance of the system.info:eu-repo/semantics/publishedVersio
Usability and cost effectiveness in brain-computer interaction: is it user throughput or technology related?
No full textIn recent years, Brain-Computer Interfaces (BCIs) have been steadily gaining ground in the market, used either as an implicit or explicit input method in computers for accessibility, entertainment or rehabilitation. Past research in BCI has heavily neglected the human aspect in the loop, focusing mostly in the machine layer. Further, due to the high cost of current BCI systems, many studies rely on low-cost and low-quality equipment with difficulties to provide significant advancements in physiological computing. OpenSource projects are offered as alternatives to expensive medical equipment. Nevertheless, the effectiveness of such systems over their cost is still unclear, and whether they can deliver the same level of experience as their more expensive counterparts. In this paper, we demonstrate that effective BCI interaction in a Motor-Imagery BCI paradigm can be accomplished without requiring high-end/high-cost devices, by analyzing and comparing EEG systems ranging from open source devices to medically certified systems.info:eu-repo/semantics/publishedVersio
Optimizing performance of non-expert users in brain-computer interaction by means of an adaptive performance engine
Full text linkBrain–Computer Interfaces (BCIs) are become increasingly more available at reduced costs and are being incorporated into immersive virtual environments and video games for serious applications. Most research in BCIs focused on signal processing techniques and has neglected the interaction aspect of BCIs. This has created an imbalance between BCI classification performance and online control quality of the BCI interaction. This results in user fatigue and loss of interest over time. In the health domain, BCIs provide a new way to overcome motor-related disabilities, promoting functional and structural plasticity in the brain. In order to exploit the advantages of BCIs in neurorehabilitation we need to maximize not only the classification performance of such systems but also engagement and the sense of competence of the user. Therefore, we argue that the primary goal should not be for users to be trained to successfully use a BCI system but to adapt the BCI interaction to each user in order to maximize the level of control on their actions, whatever their performance level is. To achieve this, we developed the Adaptive Performance Engine (APE) and tested with data from 20 naïve BCI users. APE can provide user specific performance improvements up to approx. 20% and we compare it with previous methods. Finally, we contribute with an open motor-imagery datasets with 2400 trials from naïve users.info:eu-repo/semantics/publishedVersio
Personalization of Assistance and Knowledge of Performance Feedback on a Hybrid Mobile and Myo-electric Robotic System for Motor Rehabilitation After Stroke
No full text10391130,158Q
Comparing EEG-neurofeedback visual modalities between screen-based and immersive head-mounted VR
No full textTese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas), 2022, Universidade de Lisboa, Faculdade de CiênciasNeurofeedback (NF) can be defined as a form of biofeedback that trains subjects to have self-control
over brain their functions, by providing real-time feedback of their own cerebral activity. This activity
can be presented in various forms, with auditory and visual feedback being the most common. Recently, NF has been investigated as a potential treatment for various clinical conditions associated with
abnormal brain activity or cognitive capacities. However, the greater research focus is not discussing
how the feedback should be presented. The chosen modality for any NF training system may strongly
influence the training protocol and consequently the outcome of the experiment.
In this thesis, a systematical comparison between two different type of visual modalities (ScreenBased vs. immersive-virtual reality (VR) ) was performed with the goal to evaluate the effectiveness of
each modality on the NF training results. Data from two previous studies, recorded on healthy participants, in protocols that targeted the increase in the upper alpha (UA) band power measured at the EEG
electrode Cz was used. This was then divided into two modality groups: Screen-Based modality group
(N = 8) and the Immersive-VR group (N = 4). An extensive data processing and cleaning protocol was
applied to both groups and the training effectiveness was measured through band power calculation,
the definition of learning ability indexes and the application of statistical tests. Results showed that,
both groups had a generally positive training effect within sessions, however data regarding different
sessions is inconclusive and does not show clear evidence of up-regulation of the target feature. Additionally, when only considering within-session evolution, only the Immersive-VR modality group was
able to maintain an increasing trend in all sessions.
One of the main limitations of this study was the sample size, which was too small to determine
the precise effect of NF training. Future work requires, not only an increase in sample size but also, the
definition and incorporation of learning predictors that allow the pre-selection of subjects before the
training sessions, in order to prevent high number of non-learners
Comparing EEG-neurofeedback visual modalities between screen-based and immersive head-mounted VR
No full textTese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas), 2022, Universidade de Lisboa, Faculdade de CiênciasNeurofeedback (NF) can be defined as a form of biofeedback that trains subjects to have self-control
over brain their functions, by providing real-time feedback of their own cerebral activity. This activity
can be presented in various forms, with auditory and visual feedback being the most common. Recently, NF has been investigated as a potential treatment for various clinical conditions associated with
abnormal brain activity or cognitive capacities. However, the greater research focus is not discussing
how the feedback should be presented. The chosen modality for any NF training system may strongly
influence the training protocol and consequently the outcome of the experiment.
In this thesis, a systematical comparison between two different type of visual modalities (ScreenBased vs. immersive-virtual reality (VR) ) was performed with the goal to evaluate the effectiveness of
each modality on the NF training results. Data from two previous studies, recorded on healthy participants, in protocols that targeted the increase in the upper alpha (UA) band power measured at the EEG
electrode Cz was used. This was then divided into two modality groups: Screen-Based modality group
(N = 8) and the Immersive-VR group (N = 4). An extensive data processing and cleaning protocol was
applied to both groups and the training effectiveness was measured through band power calculation,
the definition of learning ability indexes and the application of statistical tests. Results showed that,
both groups had a generally positive training effect within sessions, however data regarding different
sessions is inconclusive and does not show clear evidence of up-regulation of the target feature. Additionally, when only considering within-session evolution, only the Immersive-VR modality group was
able to maintain an increasing trend in all sessions.
One of the main limitations of this study was the sample size, which was too small to determine
the precise effect of NF training. Future work requires, not only an increase in sample size but also, the
definition and incorporation of learning predictors that allow the pre-selection of subjects before the
training sessions, in order to prevent high number of non-learners
Brain-controlled virtual environments - an evaluation study of brain-computer interfaces for serious game interaction
Full text linkUsing brain-computer interaction and multimodal virtual-reality for augmenting stroke neurorehabilitation
Full text linkEvery 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
- …
