97 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
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
Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement
As life expectancy is mostly increasing, the incidence of many neurological
disorders is also constantly growing. For improving the physical functions
affected by a neurological disorder, rehabilitation procedures are mandatory,
and they must be performed regularly. Unfortunately, neurorehabilitation
procedures have disadvantages in terms of costs, accessibility and a lack of
therapists. This paper presents Immersive Neurorehabilitation Exercises Using
Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system
using virtual reality. The system is based on a thorough research methodology
and is able to capture real-time user movements and evaluate joint mobility for
both upper and lower limbs, record training sessions and save electromyography
data. The use of the first-person perspective increases immersion, and the
joint range of motion is calculated with the help of both the HTC Vive system
and inverse kinematics principles applied on skeleton rigs. Tutorial exercises
are demonstrated by a virtual therapist, as they were recorded with real-life
physicians, and sessions can be monitored and configured through tele-medicine.
Complex movements are practiced in gamified settings, encouraging
self-improvement and competition. Finally, we proposed a training plan and
preliminary tests which show promising results in terms of accuracy and user
feedback. As future developments, we plan to improve the system's accuracy and
investigate a wireless alternative based on neural networks.Comment: 47 pages, 20 figures, 17 tables (including annexes), part of the MDPI
Sesnsors "Special Issue Smart Sensors and Measurements Methods for Quality of
Life and Ambient Assisted Living
16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)
The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc
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
EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs
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