The Design of a Customised Portable BCI Headset for Home Based Neurorehabilitation

This project aims to design a new portable BrainComputer Interface (BCI) headset that can be used by non-professionals (patients, caregivers) as part of BCI therapeuticapplications in the home environment. The suitability of thedesign of the new headset under its operating conditions wasevaluated through use of mechanical testing in ComputerAided Design (CAD) software and physical prototypes.Extensive use of CAD based design was used to create a noveltestbed for future prototypes of BCI headsets. We present asolution for optimised hardware design of the BCI headsetwhich incorporates existing electronic components (dryelectrodes, portable amplifier, battery etc.)Keywords—headset, EEG, home based, Brain ComputerInterface, CAD, desig

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

Cerebral Palsy

Nowadays, cerebral palsy (CP) rehabilitation, along with medical and surgical interventions in children with CP, leads to better motor and postural control and can ensure ambulation and functional independence. In achieving these improvements, many modern practices may be used, such as comprehensive multidisciplinary assessment, clinical decision making, multilevel surgery, botulinum toxin applications, robotic ambulation applications, treadmill, and other walking aids to increase the quality and endurance of walking. Trainings are based on neurodevelopmental therapy, muscle training and strength applications, adaptive equipment and orthotics, communication, technological solves, and many others beyond the scope of this book. In the years of clinical and academic experiences, children with cerebral palsy have shown us that the world needs a book to give clinical knowledge to health professionals regarding these important issue. This book is an attempt to fulfill and to give “current steps” about CP. The book is intended for use by physicians, therapists, and allied health professionals who treat/rehabilitate children with CP. We focus on the recent concepts in the treatment of body and structure problems and describe the associated disability, providing suggestions for further reading. All authors presented the most frequently used and accepted treatment methods with scientifically proven efficacy and included references at the end of each chapter

A virtual hand assessment system for efficient outcome measures of hand rehabilitation

Previously held under moratorium from 1st December 2016 until 1st December 2021.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control

Proceedings of the 10th international conference on disability, virtual reality and associated technologies (ICDVRAT 2014)

The proceedings of the conferenc

Proceedings of the 9th international conference on disability, virtual reality and associated technologies (ICDVRAT 2012)

The proceedings of the conferenc