Rehabilitative devices for a top-down approach

In recent years, neurorehabilitation has moved from a "bottom-up" to a "top down" approach. This change has also involved the technological devices developed for motor and cognitive rehabilitation. It implies that during a task or during therapeutic exercises, new "top-down" approaches are being used to stimulate the brain in a more direct way to elicit plasticity-mediated motor re-learning. This is opposed to "Bottom up" approaches, which act at the physical level and attempt to bring about changes at the level of the central neural system. Areas covered: In the present unsystematic review, we present the most promising innovative technological devices that can effectively support rehabilitation based on a top-down approach, according to the most recent neuroscientific and neurocognitive findings. In particular, we explore if and how the use of new technological devices comprising serious exergames, virtual reality, robots, brain computer interfaces, rhythmic music and biofeedback devices might provide a top-down based approach. Expert commentary: Motor and cognitive systems are strongly harnessed in humans and thus cannot be separated in neurorehabilitation. Recently developed technologies in motor-cognitive rehabilitation might have a greater positive effect than conventional therapies

Diseño de entornos de realidad virtual aplicables a sistemas de robótica asistencial: un análisis literario

Virtual Reality (VR) environments can be applied to assistive robotics to improve the effectiveness and the user experience perception in the rehabilitation process due to its innovative nature, getting to entertain patients while they recover their motor functions. This literature review pretends to analyze some design principles of VR environments developed for upper limb rehabilitation processes. The idea is to identify features related to peripheral and central nervous systems, types of information included as feedback to increase the user's levels of immersion having a positive impact on the user's performance and experience during the treatment. A total of 32 articles published in Scopus, IEEE, PubMed, and Web of Science in the last four years were reviewed. We present the article selection process, the division by concepts presented previously, and the guidelines that can be considered for the design of VR environments applicable to assistive robots for upper limbs rehabilitation processes.Los entornos de Realidad Virtual (RV) aplicables a sistemas de robótica asistencial pueden ser diseñados de manera que mejoren la efectividad y la experiencia de usuario de los procesos de rehabilitación debido a su naturaleza novedosa, logrando entretener a los pacientes mientras recuperan sus funciones motoras. Esta revisión literaria pretende analizar los criterios de diseño de entornos de RV utilizados en procesos de rehabilitación de miembro superior, identificando las características de entornos para rehabilitación de problemas asociados el sistema nervioso central y periféricos, los tipos de información que se realimenta al usuario para beneficiar los niveles de inmersión y su impacto en términos del desempeño y la experiencia del usuario en tratamiento. Un total de 32 artículos publicados en revistas indexadas de Scopus, IEEE, PubMed y Web of Science en los últimos cuatro años fueron revisados. Se presenta el proceso de selección de artículos, la división por las temáticas presentadas anteriormente y los lineamientos generales que pueden ser considerados para el diseño de entornos de RV aplicables a robots asistenciales en procesos de rehabilitación de miembro superior

A Multi-Modal, Modified-Feedback and Self-Paced Brain-Computer Interface (BCI) to Control an Embodied Avatar's Gait

Brain-computer interfaces (BCI) have been used to control the gait of a virtual self-avatar with the aim of being used in gait rehabilitation. A BCI decodes the brain signals representing a desire to do something and transforms them into a control command for controlling external devices. The feelings described by the participants when they control a self-avatar in an immersive virtual environment (VE) demonstrate that humans can be embodied in the surrogate body of an avatar (ownership illusion). It has recently been shown that inducing the ownership illusion and then manipulating the movements of one’s self-avatar can lead to compensatory motor control strategies. In order to maximize this effect, there is a need for a method that measures and monitors embodiment levels of participants immersed in virtual reality (VR) to induce and maintain a strong ownership illusion. This is particularly true given that reaching a high level of both BCI performance and embodiment are inter-connected. To reach one of them, the second must be reached as well. Some limitations of many existing systems hinder their adoption for neurorehabilitation: 1- some use motor imagery (MI) of movements other than gait; 2- most systems allow the user to take single steps or to walk but do not allow both, which prevents users from progressing from steps to gait; 3- most of them function in a single BCI mode (cue-paced or self-paced), which prevents users from progressing from machine-dependent to machine-independent walking. Overcoming the aforementioned limitations can be done by combining different control modes and options in one single system. However, this would have a negative impact on BCI performance, therefore diminishing its usefulness as a potential rehabilitation tool. In this case, there will be a need to enhance BCI performance. For such purpose, many techniques have been used in the literature, such as providing modified feedback (whereby the presented feedback is not consistent with the user’s MI), sequential training (recalibrating the classifier as more data becomes available). This thesis was developed over 3 studies. The objective in study 1 was to investigate the possibility of measuring the level of embodiment of an immersive self-avatar, during the performing, observing and imagining of gait, using electroencephalogram (EEG) techniques, by presenting visual feedback that conflicts with the desired movement of embodied participants. The objective of study 2 was to develop and validate a BCI to control single steps and forward walking of an immersive virtual reality (VR) self-avatar, using mental imagery of these actions, in cue-paced and self-paced modes. Different performance enhancement strategies were implemented to increase BCI performance. The data of these two studies were then used in study 3 to construct a generic classifier that could eliminate offline calibration for future users and shorten training time. Twenty different healthy participants took part in studies 1 and 2. In study 1, participants wore an EEG cap and motion capture markers, with an avatar displayed in a head-mounted display (HMD) from a first-person perspective (1PP). They were cued to either perform, watch or imagine a single step forward or to initiate walking on a treadmill. For some of the trials, the avatar took a step with the contralateral limb or stopped walking before the participant stopped (modified feedback). In study 2, participants completed a 4-day sequential training to control the gait of an avatar in both BCI modes. In cue-paced mode, they were cued to imagine a single step forward, using their right or left foot, or to walk forward. In the self-paced mode, they were instructed to reach a target using the MI of multiple steps (switch control mode) or maintaining the MI of forward walking (continuous control mode). The avatar moved as a response to two calibrated regularized linear discriminant analysis (RLDA) classifiers that used the μ power spectral density (PSD) over the foot area of the motor cortex as features. The classifiers were retrained after every session. During the training, and for some of the trials, positive modified feedback was presented to half of the participants, where the avatar moved correctly regardless of the participant’s real performance. In both studies, the participants’ subjective experience was analyzed using a questionnaire. Results of study 1 show that subjective levels of embodiment correlate strongly with the power differences of the event-related synchronization (ERS) within the μ frequency band, and over the motor and pre-motor cortices between the modified and regular feedback trials. Results of study 2 show that all participants were able to operate the cued-paced BCI and the selfpaced BCI in both modes. For the cue-paced BCI, the average offline performance (classification rate) on day 1 was 67±6.1% and 86±6.1% on day 3, showing that the recalibration of the classifiers enhanced the offline performance of the BCI (p < 0.01). The average online performance was 85.9±8.4% for the modified feedback group (77-97%) versus 75% for the non-modified feedback group. For self-paced BCI, the average performance was 83% at switch control and 92% at continuous control mode, with a maximum of 12 seconds of control. Modified feedback enhanced BCI performances (p =0.001). Finally, results of study 3 show that the constructed generic models performed as well as models obtained from participant-specific offline data. The results show that there it is possible to design a participant-independent zero-training BCI.Les interfaces cerveau-ordinateur (ICO) ont été utilisées pour contrôler la marche d'un égo-avatar virtuel dans le but d'être utilisées dans la réadaptation de la marche. Une ICO décode les signaux du cerveau représentant un désir de faire produire un mouvement et les transforme en une commande de contrôle pour contrôler des appareils externes. Les sentiments décrits par les participants lorsqu'ils contrôlent un égo-avatar dans un environnement virtuel immersif démontrent que les humains peuvent être incarnés dans un corps d'un avatar (illusion de propriété). Il a été récemment démontré que provoquer l’illusion de propriété puis manipuler les mouvements de l’égo-avatar peut conduire à des stratégies de contrôle moteur compensatoire. Afin de maximiser cet effet, il existe un besoin d'une méthode qui mesure et surveille les niveaux d’incarnation des participants immergés dans la réalité virtuelle (RV) pour induire et maintenir une forte illusion de propriété. D'autre part, atteindre un niveau élevé de performances (taux de classification) ICO et d’incarnation est interconnecté. Pour atteindre l'un d'eux, le second doit également être atteint. Certaines limitations de plusieurs de ces systèmes entravent leur adoption pour la neuroréhabilitation: 1- certains utilisent l'imagerie motrice (IM) des mouvements autres que la marche; 2- la plupart des systèmes permettent à l'utilisateur de faire des pas simples ou de marcher mais pas les deux, ce qui ne permet pas à un utilisateur de passer des pas à la marche; 3- la plupart fonctionnent en un seul mode d’ICO, rythmé (cue-paced) ou auto-rythmé (self-paced). Surmonter les limitations susmentionnées peut être fait en combinant différents modes et options de commande dans un seul système. Cependant, cela aurait un impact négatif sur les performances de l’ICO, diminuant ainsi son utilité en tant qu'outil potentiel de réhabilitation. Dans ce cas, il sera nécessaire d'améliorer les performances des ICO. À cette fin, de nombreuses techniques ont été utilisées dans la littérature, telles que la rétroaction modifiée, le recalibrage du classificateur et l'utilisation d'un classificateur générique. Le projet de cette thèse a été réalisé en 3 études, avec objectif d'étudier dans l'étude 1, la possibilité de mesurer le niveau d'incarnation d'un égo-avatar immersif, lors de l'exécution, de l'observation et de l'imagination de la marche, à l'aide des techniques encéphalogramme (EEG), en présentant une rétroaction visuelle qui entre en conflit avec la commande du contrôle moteur des sujets incarnés. L'objectif de l'étude 2 était de développer un BCI pour contrôler les pas et la marche vers l’avant d'un égo-avatar dans la réalité virtuelle immersive, en utilisant l'imagerie motrice de ces actions, dans des modes rythmés et auto-rythmés. Différentes stratégies d'amélioration des performances ont été mises en œuvre pour augmenter la performance (taux de classification) de l’ICO. Les données de ces deux études ont ensuite été utilisées dans l'étude 3 pour construire des classificateurs génériques qui pourraient éliminer la calibration hors ligne pour les futurs utilisateurs et raccourcir le temps de formation. Vingt participants sains différents ont participé aux études 1 et 2. Dans l'étude 1, les participants portaient un casque EEG et des marqueurs de capture de mouvement, avec un avatar affiché dans un casque de RV du point de vue de la première personne (1PP). Ils ont été invités à performer, à regarder ou à imaginer un seul pas en avant ou la marche vers l’avant (pour quelques secondes) sur le tapis roulant. Pour certains essais, l'avatar a fait un pas avec le membre controlatéral ou a arrêté de marcher avant que le participant ne s'arrête (rétroaction modifiée). Dans l'étude 2, les participants ont participé à un entrainement séquentiel de 4 jours pour contrôler la marche d'un avatar dans les deux modes de l’ICO. En mode rythmé, ils ont imaginé un seul pas en avant, en utilisant leur pied droit ou gauche, ou la marche vers l’avant . En mode auto-rythmé, il leur a été demandé d'atteindre une cible en utilisant l'imagerie motrice (IM) de plusieurs pas (mode de contrôle intermittent) ou en maintenir l'IM de marche vers l’avant (mode de contrôle continu). L'avatar s'est déplacé en réponse à deux classificateurs ‘Regularized Linear Discriminant Analysis’ (RLDA) calibrés qui utilisaient comme caractéristiques la densité spectrale de puissance (Power Spectral Density; PSD) des bandes de fréquences µ (8-12 Hz) sur la zone du pied du cortex moteur. Les classificateurs ont été recalibrés après chaque session. Au cours de l’entrainement et pour certains des essais, une rétroaction modifiée positive a été présentée à la moitié des participants, où l'avatar s'est déplacé correctement quelle que soit la performance réelle du participant. Dans les deux études, l'expérience subjective des participants a été analysée à l'aide d'un questionnaire. Les résultats de l'étude 1 montrent que les niveaux subjectifs d’incarnation sont fortement corrélés à la différence de la puissance de la synchronisation liée à l’événement (Event-Related Synchronization; ERS) sur la bande de fréquence μ et sur le cortex moteur et prémoteur entre les essais de rétroaction modifiés et réguliers. L'étude 2 a montré que tous les participants étaient capables d’utiliser le BCI rythmé et auto-rythmé dans les deux modes. Pour le BCI rythmé, la performance hors ligne moyenne au jour 1 était de 67±6,1% et 86±6,1% au jour 3, ce qui montre que le recalibrage des classificateurs a amélioré la performance hors ligne du BCI (p <0,01). La performance en ligne moyenne était de 85,9±8,4% pour le groupe de rétroaction modifié (77-97%) contre 75% pour le groupe de rétroaction non modifié. Pour le BCI auto-rythmé, la performance moyenne était de 83% en commande de commutateur et de 92% en mode de commande continue, avec un maximum de 12 secondes de commande. Les performances de l’ICO ont été améliorées par la rétroaction modifiée (p = 0,001). Enfin, les résultats de l'étude 3 montrent que pour la classification des initialisations des pas et de la marche, il a été possible de construire des modèles génériques à partir de données hors ligne spécifiques aux participants. Les résultats montrent la possibilité de concevoir une ICO ne nécessitant aucun entraînement spécifique au participant

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

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

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

Impact of virtual embodiment and exercises on functional ability and range of motion in orthopedic rehabilitation

Recent evidence supports the use of immersive virtual reality (immersive VR) as a means of applying visual feedback techniques in neurorehabilitation. In this study, we investigated the benefits of an embodiment-based immersive VR training program for orthopedic upper limb rehabilitation, with the aim of improving the motor functional ability of the arm and accelerating the rehabilitation process in patients with a conservatively managed distal radius fracture. We designed a rehabilitation program based on developing ownership over a virtual arm and then exercising it in immersive VR. We carried out a between 3-group controlled trial with 54 patients (mean age = 61.80 ± 14.18): 20 patients were assigned to the experimental training group (immersive VR), 20 to the conventional digit mobilization (CDM) training control group, and 14 to a non-immersive (non-immersive VR) training control group. We found that functional recovery of the arm in the immersive VR group was correlated with the ownership and agency scores over the virtual arm. We also found larger range of joint movements and lower disability of the fractured arm compared with patients in the Non-immersive VR and CDM groups. Feeling embodied in a virtual body can be used as a rehabilitation tool to speed up and improve motor functional recovery of a fractured arm after the immobilization period

Head-mounted display-based therapies for adults post-stroke: A systematic review and meta-analysis

Immersive virtual reality techniques have been applied to the rehabilitation of patients after stroke, but evidence of its clinical effectiveness is scarce. The present review aims to find studies that evaluate the effects of immersive virtual reality (VR) therapies intended for motor function rehabilitation compared to conventional rehabilitation in people after stroke and make recommendations for future studies. Data from different databases were searched from inception until October 2020. Studies that investigated the effects of immersive VR interventions on poststroke adult subjects via a head-mounted display (HMD) were included. These studies included a control group that received conventional therapy or another non-immersive VR intervention. The studies reported statistical data for the groups involved in at least the posttest as well as relevant outcomes measuring functional or motor recovery of either lower or upper limbs. Most of the studies found significant improvements in some outcomes after the intervention in favor of the virtual rehabilitation group. Although evidence is limited, immersive VR therapies constitute an interesting tool to improve motor learning when used in conjunction with traditional rehabilitation therapies, providing a non-pharmacological therapeutic pathway for people after stroke

3D visualization of movements can amplify motor cortex activation during subsequent motor imagery

A repetitive movement practice by motor imagery (MI) can influence motor cortical excitability in the electroencephalogram (EEG). This study investigated if a realistic visualization in 3D of upper and lower limb movements can amplify motor related potentials during subsequent MI. We hypothesized that a richer sensory visualization might be more effective during instrumental conditioning, resulting in a more pronounced event related desynchronization (ERD) of the upper alpha band (10–12 Hz) over the sensorimotor cortices thereby potentially improving MI based brain-computer interface (BCI) protocols for motor rehabilitation. The results show a strong increase of the characteristic patterns of ERD of the upper alpha band components for left and right limb MI present over the sensorimotor areas in both visualization conditions. Overall, significant differences were observed as a function of visualization modality (VM; 2D vs. 3D). The largest upper alpha band power decrease was obtained during MI after a 3-dimensional visualization. In total in 12 out of 20 tasks the end-user of the 3D visualization group showed an enhanced upper alpha ERD relative to 2D VM group, with statistical significance in nine tasks.With a realistic visualization of the limb movements, we tried to increase motor cortex activation during subsequent MI. The feedback and the feedback environment should be inherently motivating and relevant for the learner and should have an appeal of novelty, real-world relevance or aesthetic value (Ryan and Deci, 2000; Merrill, 2007). Realistic visual feedback, consistent with the participant’s MI, might be helpful for accomplishing successful MI and the use of such feedback may assist in making BCI a more natural interface for MI based BCI rehabilitation