Posts Tagged BCI

[Abstract] A brain–computer interface based stroke rehabilitation system, controlling an avatar and functional electrical stimulation, to improve motor functions

Introduction/Background

Brain–computer interfaces (BCI) can detect the neuronal activity of patients’ motor intention to control external devices. With the feedback from the device, the neuronal network in the brain to reorganizes due to neuroplasticity.

Material and method

The BCI controls an avatar and functional electrical stimulation (FES) to provide the feedback. The expected task for the patient is to imagine either left or right wrist dorsiflexion according to the instructions. The training was designed to have 25 sessions (240 trials of either left or right motor imagery) of BCI feedback sessions over 13 weeks. Two days before and two days after we did clinical measures to observe motor improvement. The primary measure was upper extremity Fugl–Meyer assessment (UE-FMA), which evaluates the motor impairment. Four secondary measures were also performed to exam the spasm (modified Ashworth scale, MAS), tremor (Fahn tremor rating scale, FTRS), level of daily activity (Barthel index, BI), and finger dexterity (9-hole peg test, 9HPT).

Results

One male stroke patient (53 years old, 11 months since stroke, and right upper limb paralyzed) participated in the training. He quickly learned to use the BCI and the maximal classification accuracy was over 90% after the 5th session. The UE-FMA increased from 25 to 46 points. The BI increased from 90 to 95 points. MAS and FTRS decreased from 2 to 1 and from 4 to 3 points respectively. Although he could not conduct the 9HPT until 18th training session, he was able to complete the test from 19th session in 10 min 22 s and the time was reduced to 2 min 53 s after 25th session.

Conclusion

The patient could be more independent in his daily activity, he had less spasticity and tremor. Also, the 9HPT was possible to do, which was not before. The system is currently validated with a study of 50 patients.

 

via A brain–computer interface based stroke rehabilitation system, controlling an avatar and functional electrical stimulation, to improve motor functions – ScienceDirect

Advertisements

, , , , , , , , , , ,

Leave a comment

[WEB SITE] Scientists develop combined therapy for stroke victim recovery

Scientists in Switzerland have demonstrated that combining a brain-computer interface (BCI) with functional electrical stimulation (FES) can help stroke victims recover greater use of their paralysed limbs – even years after the stroke.

 

stroke-brain-computer-interface

 

Paralysis of an arm and/or leg is one of the most common results of a stroke. However, a team of scientists at the Defitech Foundation Chair in Brain-Machine Interface, in association with other members of EPFL’s Center for Neuroprosthetics, the Clinique Romande de Réadaptation in Sion, and the Geneva University Hospitals, have developed a technique aimed at enabling stroke victims to recover greater use of their paralysed limbs. The scientists’ pioneering approach utilises two existing therapies – a brain-computer interface (BCI) and functional electrical stimulation (FES).

Explaining the key to their approach, José del R. Millán, who holds the Defitech Chair at EPFL, said: “The key is to stimulate the nerves of the paralysed arm precisely when the stroke-affected part of the brain activates to move the limb, even if the patient can’t actually carry out the movement. That helps re-establish the link between the two nerve pathways where the signal comes in and goes out.”.

Combined therapy tested on stroke patients

Twenty-seven patients aged between 36 and 76 took part in the clinical trial. All had a similar lesion that resulted in moderate to severe arm paralysis following a stroke occurring at least ten months earlier. Half of the patients were treated with the scientists’ dual-therapy approach and reported clinically significant improvements. The other half were treated only with FES and served as a control group.

For the first group, the scientists used a BCI system to link the patients’ brains to computers by means of electrodes. This enabled them to pinpoint exactly where the electrical activity occurred in the brain tissue when the patients tried to reach out their hands. Each time the electrical activity was identified the system immediately stimulated the arm muscle controlling the corresponding wrist and finger movements. The patients in the second group also had their arm muscles stimulated, but at random times. This control group enabled the scientists to determine how much of the additional motor-function improvement could be attributed to the BCI system.

 

The scientists noted a significant improvement in arm mobility among patients in the first group after just ten one-hour sessions. When the full round of treatment was completed, some of the first-group patients’ scores on the Fugl-Meyer Assessment – a test used to evaluate motor recovery among patients with post-stroke hemiplegia – were over twice as high as those of the second group.

“Patients who received the BCI treatment showed more activity in the neural tissue surrounding the affected area. Due to their plasticity, they could help make up for the functioning of the damaged tissue,” says Millán.

 

Electroencephalographies (EEGs) of the patients clearly showed an increase in the number of connections among the motor cortex regions of their damaged brain hemisphere, which corresponded with the increased ease in carrying out the associated movements. In addition, the enhanced motor function didn’t seem to diminish with time. Evaluated again 6-12 months later, the patients were found to have lost none of their recovered mobility.

The study results were published in Nature Communications.

via Scientists develop combined therapy for stroke victim recovery

, , , , , , , , ,

1 Comment

[Case Report] Biomechanical Assessment of Fugl-Meyer Score: The Case of One Post Stroke Patient Who has Undergone the Rehabilitation using Hand Exoskeleton Controlled by Brain-Computer Interface

Abstract

Objective: The study is double aimed: 1) to propose a version of common protocol for an assessment of upper limb motor impairment with the use of biomechanical characteristics of Fugl-Meyer items and 2) to apply this protocol to assess an efficacy of rehabilitation using hand exoskeleton controlled by brain-computer interface during the late stage of post stroke recovery in patient with mild paresis.

Methods: One patient, 62 years old man, 10 months after ischemic stroke was recruited in the rehabilitation procedure. The patient was instructed to perform one of three tasks: to relax and to imagine kinesthetically slow extension of either paretic (left) or intact (right) hand fingers. The recorded electroencephalography was analyzed and exoskeleton extended the patient’s fingers if brain-computer interface classifier recognized the imagery of their extension. The patient performed 10 daily procedures, each including three 10-minute long sessions. 14 items of Fugl-Meyer scale, describing flexor synergy (domain II), extensor synergy (domain III), movement combining synergies (domain IV) and movement out of synergy (domain V) were evaluated by standard Fugl-Meyer scores. In addition to Fugl-Meyer assessment biomechanical analysis of each item was performed. The items were recorded by electromagnetic tracking system for both paretic and intact arms. All seven degrees of freedom in each
arm were taken into account. Two types of biomechanical parameters were analyzed: 1) coordination between angular velocities and 2) maximal angular velocities corresponding to seven degrees of freedom of the arm.

Results: Fugl-Meyer assessment revealed motor improvements for two items only, whereas biomechanical analysis for all 14 items considered.

Conclusion: The use of Fugl-Meyer scale completed by biomechanical parameters of its’ items can be a version of common protocol for assessment of upper limb motor impairment, useful for obtaining a comparable data in different clinical studies.

Full Text PDF

, , , , , , , ,

1 Comment

[ARTICLE] Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke – Full Text

Abstract

Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor recovery in chronic stroke survivors more effectively than sham FES. Such recovery is associated to quantitative signatures of functional neuroplasticity. BCI patients exhibit a significant functional recovery after the intervention, which remains 6–12 months after the end of therapy. Electroencephalography analysis pinpoints significant differences in favor of the BCI group, mainly consisting in an increase in functional connectivity between motor areas in the affected hemisphere. This increase is significantly correlated with functional improvement. Results illustrate how a BCI–FES therapy can drive significant functional recovery and purposeful plasticity thanks to contingent activation of body natural efferent and afferent pathways.

Introduction

Despite considerable efforts over the last decades, the quest for novel treatments for arm functional recovery after stroke remains a priority1. Synergistic efforts in neural engineering and restoration medicine are demonstrating how neuroprosthetic approaches can control devices and ultimately restore body function2,3,4,5,6,7. In particular, non-invasive brain-computer interfaces (BCI) are reaching their technological maturity8,9 and translate neural activity into meaningful outputs that might drive activity-dependent neuroplasticity and functional motor recovery10,11,12. BCI implies learning to modify the neuronal activity through progressive practice with contingent feedback and reward —sharing its neurobiological basis with rehabilitation13.

Most attempts to use non-invasive BCI systems for upper limb rehabilitation after stroke have coupled them with other interventions, although not all trials reported clinical benefits. The majority of these studies are case reports of patients who operated a BCI to control either rehabilitation robots14,15,16,17,18,19 or functional electrical stimulation (FES)20,21,22,23. A few works have described changes in functional magnetic resonance imaging (fMRI) that correlate with motor improvements17,18,22.

Recent controlled trials have shown the potential benefit of BCI-based therapies24,25,26,27. Pichiorri et al.26recruited 28 subacute patients and studied the efficacy of motor imagery with or without BCI support via visual feedback, reporting a significant and clinically relevant functional recovery for the BCI group. As a step forward in the design of multimodal interventions, BCI-aided robotic therapies yielded significantly greater motor gains than robotic therapies alone24,25,27. In the first study, involving 30 chronic patients24, only the BCI group exhibited a functional improvement. In the second study, involving 14 subacute and chronic patients, both groups improved, probably reflecting the larger variance in subacute patients’ recovery and a milder disability25. The last study27 showed that in a mixed population of 74 subacute and chronic patients, the percentage of patients who achieved minimally clinical important difference in upper limb functionality was higher in the BCI group. The effect in favor of the BCI group was only evident in the sub-population of chronic patients. Moreover, the conclusions of this study are limited due to differences between experimental and control groups prior to the intervention, such as number of patients and FMA-UE scores, which were always in favor of the BCI group.

In spite of promising results achieved so far, BCI-based stroke rehabilitation is still a young field where different works report variable clinical outcomes. Furthermore, the efficacy and mechanisms of BCI-based therapies remain largely unclear. We hypothesize that, for BCI to boost beneficial functional activity-dependent plasticity able to attain clinically important outcomes, the basic premise is contingency between suitable motor-related cortical activity and rich afferent feedback. Our approach is designed to deliver associated contingent feedback that is not only functionally meaningful (e.g., via virtual reality or passive movement of the paretic limb by a robot), but also tailored to reorganize the targeted neural circuits by providing rich sensory inputs via the natural afferent pathways28, so as to activate all spare components of the central nervous system involved in motor control. FES fulfills these two properties of feedback contingent on appropriate patterns of neural activity; it elicits functional movements and conveys proprioceptive and somatosensory information, in particular via massive recruitment of Golgi tendon organs and muscle spindle feedback circuits. Moreover, several studies suggest that FES has an impact on cortical excitability29,30.

To test our hypothesis, this study assessed whether BCI-actuated FES therapy targeting the extension of the affected hand (BCI–FES) could yield stronger and clinically relevant functional recovery than sham-FES therapy for chronic stroke patients with a moderate-to-severe disability, and whether signatures of functional neuroplasticity would be associated with motor improvement. Whenever the BCI decoded a hand-extension attempt, it activated FES of the extensor digitorum communis muscle that elicited a full extension of the wrist and fingers. Patients in the sham-FES group wore identical hardware and received identical instructions as BCI–FES patients, but FES was delivered randomly and not driven by neural activity.

As hypothesized, our results confirm that only the BCI group exhibit a significant functional recovery after the intervention, which is retained 6–12 months after the end of therapy. Besides the main clinical findings, we have also attempted to shed light on possible mechanisms underlying the proposed intervention. Specifically, electroencephalography (EEG) imaging pinpoint significant differences in favor of the BCI group, mainly an increase in functional connectivity between motor areas in the affected hemisphere. This increase is significantly correlated with functional improvement. Furthermore, analysis of the therapeutic sessions substantiates that contingency between motor-related brain activity and FES occurs only in the BCI group and contingency-based metrics correlate with the functional improvement and increase in functional connectivity, suggesting that our BCI intervention might have promoted activity-dependent plasticity.[…]

Continue —> Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke | Nature Communications

, , , , , , , , , , , ,

Leave a comment

[BLOG POST] A Dual-Therapy Approach to Boost Motor Recovery After a Stroke

Stroke victims have a reason to finally smile as a new therapy approach promises to help them recover greater use of their paralyzed leg and/or arm. In a recent study, researchers managed to demonstrate that indeed, broken sensory nerve connections can be reconstructed without surgery, but using two therapies at the same time.

The technique combines functional electrical stimulation (FES) and brain-computer interface BCI to help “resurrect” the use of paralyzed limb (arm/leg). In most cases, paralysis happens to be the most general but hard to bear effects of a stroke. Fortunately, research now seems to have a solution for treating this effect.

Communication Between Nerve Pathways

While the approach may not be something completely out of the horizon, this is the first time experts considered deploying two therapies at the same time on stroke effects (FES + BCI). The therapy works to help reestablish communication between nerve pathways, which ideally corrects how signals come in and go out of the nerve segment endings.

“The goal is to stimulate those nerves thought to have been silenced by the paralysis. This should be the work of the brain. But as the part of the brain tasked to do this may no longer be active enough, the therapy steps in to help reestablish the links between (the brain) and the nerve pathways,” explains Jose del R. Millan, one of the scientists involve in the research, which was pioneered by the Defitech Foundation Brain and Machine Interface.

Degrees of Paralysis

The work, which also appears in the latest issue of Nature Communications focused on mid-age and aged adults of between 36 to 76, and involved 27 volunteers with varying stroke effects. A section of the patients had moderate paralysis, while for the rest the cases were considered as severe arm paralysis occurring less than a year prior to the dual-therapy.

Representing half of the volunteering team, 14 of the patients took the dual-therapy and the results found a significant lasting improvement in the ability to initiate control of their affected arms. The other half of the volunteers took the functional electrical stimulation (FES) treatment only and acted as a control team to help monitor progress.

Hunting for the Brain Signals

Now, the scientists introduced the BCI system to access the patient’s brain response, linking the same to computers via electrodes. The exact task was to pinpoint the specific areas the electrical signals showed up in the brain as the patient tried to pick something using the affected arm.

When the electrical activity was spotted the system immediately stimulated the concerned muscle in the wrist and finger to have it respond to the signal. Patients in the “control” group had their muscles stimulated but not as often as the first team. That was done on purpose to help establish the motor-function improvement that could directly be attributed to the BCI system and the reliability of the same.

Reactivated Tissue and How this Changes Stroke Effect Therapy

Source: braceworks

What makes the research outstanding is that some patients in the first group registered a significant improvement in arm mobility within the first ten one-hour therapy sessions. Using a special test that evaluates motor recovery on post-stroke hemiplegia, called the Fugl-Meyer Assessment, a good number of the patients in the first group improved in their mobility twice in score compared to their counterparts.

The scientists also found an overall increase in connection among the motor cortex areas of their damaged brain, which corresponded with the overall ease in undertaking the associated tasks.

This might, without doubt, be the complete game changer of the way effects of stroke should be treated, because, even after 6 and 12 months – looking at the progress of the patients, their recovered mobility from the dual-therapy was maintained.

 

via A Dual-Therapy Approach to Boost Motor Recovery After a Stroke – Sanvada

, , , , , ,

Leave a comment

[ARTICLE] BCI-Based Strategies on Stroke Rehabilitation with Avatar and FES Feedback – Full Text PDF

Stroke is the leading cause of serious and long-term disability worldwide. Some studies have shown that motor imagery (MI) based BCI has a positive effect in poststroke rehabilitation. It could help patients promote the reorganization processes in the damaged brain regions. However, offline motor imagery and conventional online motor imagery with feedback (such as rewarding sounds and movements of an avatar) could not reflect the true intention of the patients. In this study, both virtual limbs and functional electrical stimulation (FES) were used as feedback to provide patients a closed-loop sensorimotor integration for motor rehabilitation. The FES system would activate if the user was imagining hand movement of instructed side. Ten stroke patients (7 male, aged 22-70 years, mean 49.5+-15.1) were involved in this study. All of them participated in BCI-FES rehabilitation training for 4 weeks.The average motor imagery accuracies of the ten patients in the last week were 71.3%, which has improved 3% than that in the first week. Five patients’ Fugl-Meyer Assessment (FMA) scores have been raised. Patient 6, who has have suffered from stroke over two years, achieved the greatest improvement after rehabilitation training (pre FMA: 20, post FMA: 35). In the aspect of brain patterns, the active patterns of the five patients gradually became centralized and shifted to sensorimotor areas (channel C3 and C4) and premotor area (channel FC3 and FC4).In this study, motor imagery based BCI and FES system were combined to provided stoke patients with a closed-loop sensorimotor integration for motor rehabilitation. Result showed evidences that the BCI-FES system is effective in restoring upper extremities motor function in stroke. In future work, more cases are needed to demonstrate its superiority over conventional therapy and explore the potential role of MI in poststroke rehabilitation.

Download: PDF only

via [1805.04986] BCI-Based Strategies on Stroke Rehabilitation with Avatar and FES Feedback

, , , , , , , , , , ,

Leave a comment

[ARTICLE] Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond – Full Text

Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.

Introduction

According to the World Health Organization (WHO), neurological disorders and injuries account for the 6.3% of the global burden of disease (GBD) (12). With more than 6% of DALY (disability-adjusted life years) in the world, neurological disorders represent one of the most widespread clinical condition. Among neurological disorders, more than half of the burden in DALYs is constituted by cerebral-vascular disease (55%), such as stroke. Stroke, together with spinal cord injury (SCI), accounts for 52% of the adult-onset disability and, over a billion people (i.e., about a 15% of the population worldwide) suffer from some form of disability (3). These numbers are likely to increase in the coming years due to the aging of the population (4), since disorders affecting people aged 60 years and older contribute to 23% of the total GBD (5).

Standard physical rehabilitation favors the functional recovery after stroke, as compared to no treatment (6). However, the functional recovery is not always satisfactory as only 20% of patients fully resume their social life and job activities (7). Hence, the need of more effective and patient-tailored rehabilitative approaches to maximize the functional outcome of neurological injuries as well as patients’ quality of life (8). Modern technological methodologies represent one of the most recent advances in neurorehabilitation, and an increasing body of evidence supports their role in the recovery from brain and/or medullary insults. This manuscript provides a perspective on how technologies and methodologies could be combined in order to maximize the outcome of neurorehabilitation.

Current Systems and Therapeutic Approaches for Neurorehabilitation

The great progress made in interdisciplinary fields, such as neural engineering (910), has allowed to investigate many neural mechanisms, by detecting and processing the neural signals at high spatio-temporal resolution, and by interfacing the nervous system with external devices, thus restoring neurological functions lost due to disease/injury. The progress continues in parallel to technological advancements. The last two decades there has seen a large proliferation of technological approaches for human rehabilitation, such as robots, wearable systems, brain stimulation, and virtual environments. In the next sections, we will focus on: robotic therapy, non-invasive brain stimulation (NIBS), and neural interfaces.

Robotic Devices

Robots for neurorehabilitation are designed to support the administration of physical exercises to the upper or lower extremities, with the purpose of promoting neuro-motor recovery. This technology has a relatively long history, dating back to the early 1990s (11). Robot devices for rehabilitation differ widely in terms of mechanical design, number of degrees of freedom, and control architectures. As regards the mechanical design, robots may have either a single point of interaction (i.e., end effector) with the user body (endpoint robots or manipulanda) or multiple points of interaction (exoskeletons and wearable robots) (12).

Endpoint robots for the upper extremity, include Inmotion2 (IMT, USA) (13), KINARM End-Point (BKIN, Canada), and Braccio di Ferro (14) (Figure 1A1, left). Only some of these devices have been tested in randomized clinical trials (15), confirming an improvement of upper limb motor function after stroke (16). However, convincing evidence in favor of significant changes in activities of daily living (ADL) indicators is lacking (17), possibly because performance in ADL is highly affected by hand functionality. A good example of lower limb endpoint robot is represented by gait trainer GT1 (Reha-Stim, Germany). Its efficacy was tested by Picelli et al. (18), who demonstrated an improvement in multiple clinical measures in subjects with Parkinson’s disease following robotic-assisted rehabilitation when compared to physical rehabilitation alone (18). Endpoint robots are also available for postural rehabilitation. For instance, Hunova (Movendo Technology, Italy, launched in 2017) is equipped with a seat and a platform that induce multidirectional movements to improve postural stability (Figure 1A1, right).

 

Figure 1. Neurorehabilitation therapies. (A1) Endpoint robots: on the left the “Braccio di Ferro” manipulandum, on the right the postural robot Hunova. Braccio di ferro (14) is a planar manipulandum with 2-DOF, developed at the University of Genoa (Italy). It is equipped with direct-drive brushless motors and is specially designed to minimize endpoint inertia. It uses the H3DAPI programming environment, which allows to share exercise protocol with other devices. Written informed consent was obtained from the subject depicted in the panel. Movendo Technology’s Hunova is a robotic device that permits full-body rehabilitation. It has two 2-DOF actuated and sensorized platforms located under the seat and on the floor level that allow it to rehabilitate several body districts, including lower limb (thanks to the floor-level platform), the core, and the back, using the platform located underneath the seat. Different patient categories (orthopedic, neurological, and geriatric) can be treated, and interact with the machine through a GUI based on serious games. (A2) Wearable device: the recent exoskeleton Twin. Twin is a fully modular device developed at IIT and co-funded by INAIL (the Italian National Institute for Insurance against Accidents at Work). The device can be easily assembled/disassembled by the patient/therapist. It provides total assistance to patients in the 5–95th percentile range with a weight up to 110 kg. Its modularity is implemented by eight quick release connectors, each located at both mechanical ends of each motor, that allow mechanical and electrical connection with the rest of the structure. It can implement three different walking patterns that can be fully customized according to the patient’s needs viaa GUI on mobile device, thus enabling personalization of the therapy. Steps can be triggered via an IMU-based machine state controller. (B1) Repetitive transcranial magnetic stimulation (rTMS) representation. rTMS refers to the application of magnetic pulses in a repetitive mode. Conventional rTMS applied at low frequency (0.2–1 Hz) results in plastic inhibition of cortical excitability, whereas when it is applied at high frequency (≥5Hz), it leads to excitation (19). rTMS can also be applied in a “patterned mode.” Theta burst stimulation involves applying bursts of high frequency magnetic stimulation (three pulses at 50 Hz) repeated at intervals of 200 ms (20). Intermittent TBS increases cortical excitability for a period of 20–30 min, whereas continuous TBS leads to a suppression of cortical activity for approximately the same amount of time (20). (B2) Transcranial current stimulation (tCS) representation. tCS uses ultra-low intensity current, to manipulate the membrane potential of neurons and modulate spontaneous firing rates, but is insufficient on its own to discharge resting neurons or axons (21). tCS is an umbrella term for a number of brain modulating paradigms, such as transcranial direct current stimulation (22), transcranial alternating current stimulation (23), and transcranial random noise stimulation (24). (C) A typical BCI system. Five stages are represented: brain-signal acquisition, preprocessing, feature extraction/selection, classification, and application interface. In the first stage, brain-signal acquisition, suitable signals are acquired using an appropriate modality. Since the acquired signals are normally weak and contain noise (physiological and instrumental) and artifacts, preprocessing is needed, which is the second stage. In the third stage, some useful data or so-called “features” are extracted. These features, in the fourth stage, are classified using a suitable classifier. Finally, in the fifth stage, the classified signals are transmitted to a computer or other external devices for generating the desired control commands to the devices. In neurofeedback applications, the application interface is a real-time display of brain activity, which enables self-regulation of brain functions (25).

Continue —> Frontiers | Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond | Neurology

, , , , , , ,

Leave a comment

[ARTICLE] BCI and FES Based Therapy for Stroke Rehabilitation Using VR Facilities – Full Text

Abstract

In recent years, the assistive technologies and stroke rehabilitation methods have been empowered by the use of virtual reality environments and the facilities offered by brain computer interface systems and functional electrical stimulators. In this paper, a therapy system for stroke rehabilitation based on these revolutionary techniques is presented. Using a virtual reality Oculus Rift device, the proposed system ushers the patient in a virtual scenario where a virtual therapist coordinates the exercises aimed at restoring brain function. The electrical stimulator helps the patient to perform rehabilitation exercises and the brain computer interface system and an electrooculography device are used to determine if the exercises are executed properly. Laboratory tests on healthy people led to system validation from technical point of view. The clinical tests are in progress, but the preliminary results of the clinical tests have highlighted the good satisfaction degree of patients, the quick accommodation with the proposed therapy, and rapid progress for each user rehabilitation.

1. Introduction

The worldwide statistics reported by World Health Organization highlight that stroke is the third leading cause of death and about 15 million people suffer stroke worldwide each year ‎[1]. Of these, 5 million are permanently disabled needing long time assistance and only 5 million are considered socially integrated after recovering. Recovering from a stroke is a difficult and long process that requires patience, commitment, and access to various assistive technologies and special devices. Rehabilitation is an important part of recovering and helps the patient to keep abilities or gain back lost abilities in order to become more independent. Taking into account the depression installed after stroke, it is very important for a patient to benefit from an efficient and fast rehabilitation program followed by a quick return to community living ‎[2]. In the last decade, many research groups are focused on motor, cognitive, or speech recovery after stroke like Stroke Centers from Johns Hopkins Institute ‎[3], ENIGMA-Stroke Recovery ‎[4], or StrokeBack Consortium funded by European Union’s Seventh Framework Programme ‎[5]. Important ICT companies bring a major contribution to the development of technologies and equipment that can be integrated into rehabilitation systems. For example, Stroke Recovery with Kinect is a research project to build an interactive and home-rehabilitation system for motor recovery after a stroke based on Microsoft Kinect technology ‎[6].

In the last years, the virtual reality (VR) applications received a boost in development due to VR headset prices that dropped below $1000, allowing them to become a mass-market product ‎[7]. The VR was and still is especially used for military training or video games to provide some sense of realism and interaction with the virtual environment to its users ‎[8]. Now it attracts more and more the interest of physicians and therapist which are exploring the potential of VR headset and augmented reality (AR) to improve the neuroplasticity of the brain, to be used in neurorehabilitation and treatment of motor/mental disorders ‎[9]. However, considering the diversity of interventions and methods used, there is no evidence that VR therapy alone can be efficacious compared with other traditional therapies for a particular type of impairment ‎[10]. This does not mean that the potential of VR was overestimated and the results are not the ones that were expected. The VR therapy must be complemented with other forms of rehabilitation technologies like robotic therapy, brain computer interface (BCI) and functional electrical stimulation (FES) therapy, and nevertheless traditional therapy to provide a more targeted approach ‎[11].

SaeboVR is a virtual rehabilitation system exclusively focusing on activities of daily living and uses a virtual assistant that appears on the screen to educate and facilitate performance by providing real-time feedback ‎[12]. The neurotechnology company MindMaze has introduced MindMotion PRO, a 3D virtual environment therapy for upper limb neurorehabilitation incorporating virtual reality-based physical and cognitive exercise games into stroke rehabilitation programs ‎[13]. At New York Dynamic Neuromuscular Rehabilitation, the CAREN (Computer Assisted Rehabilitation Environment) based on VR is currently used to treat patients poststroke and postbrains injuries ‎[14]. EVREST Multicentre has achieved remarkable results regarding the use of VR exercises in stroke rehabilitation ‎[15].

Motor imagery (MI) is a technique used in poststroke rehabilitation for a long time ago. One of its major problems was that there was not an objective method to determine whether the user is performing the expected movement imagination. MI-based BCIs can quantify the motor imagery and output signals that can be used for controlling an external device such as a wheelchair, neuroprosthesis, or computer. The FES therapy combined with MI-based BCI became a promising technique for stroke rehabilitation. Instead of providing communication, in this case, MI is used to induce closed-loop feedback within conventional poststroke rehabilitation therapy. This approach is called paired stimulation (PS) due to the fact that it pairs each user’s motor imagery with stimulation and feedback, such as activation of a functional electrical stimulator (FES), avatar movement, and/or auditory feedback ‎[16]. Recent research from many groups showed that MI can be recorded in the clinical environment from patients and used to control real-time feedback and at the same time, they support the hypothesis that PS could improve the rehabilitation therapy outcome ‎[1721].

In a recent study, Irimia et al. ‎[22] have proved the efficacy of combining motor imagery, bar feedback, and real hand movements by testing a system combining a MI-based BCI and a neurostimulator on three stroke patients. In every session, the patients had to imagine 120 left-hand and 120 right-hand movements. The visual feedback was provided in form of an extending bar on the screen. During the trials where the correct imagination was classified, the FES was activated in order to induce the opening of the corresponding hand. All patients achieved high control accuracies and exhibited improvements in motor function. In a later study, Cho et al. ‎[23] present the results of two patients who performed the BCI training with first-person avatar feedback. After the study, both patients reported improvements in motor functions and both have improved their scores on Upper Extremity Fugl-Meyer Assessment scale. Even if the number of patients presented in these two studies is low, they support the idea that this kind of systems may bring additional benefits to the rehabilitation process outcome in stroke patients.

2. General System Architecture

The BCI-FES technique presented in this paper is part of a much more complex system designed for stroke rehabilitation called TRAVEE ‎[24], presented in Figure 1. The stimulation devices, the monitoring devices, the VR headset, and a computer running the software are the main modules of the TRAVEE system. The stimulation devices help the patient to perform the exercises and the monitoring devices are used to determine if the exercises are executed properly, according to the proposed scenarios. Actually, the TRAVEE system must be seen as a software kernel that allows defining a series of rehabilitation exercises using a series of USB connectable devices. This approach is very useful because it offers the patient the options to buy, borrow, or rent the abovementioned devices according to his needs and after connection, the therapist may choose the suitable set of exercises.[…]

 

Continue —> BCI and FES Based Therapy for Stroke Rehabilitation Using VR Facilities

, , , , , , , ,

Leave a comment

[WEB SITE] DARPA Is Planning to Hack the Human Brain to Let Us “Upload” Skills

IN BRIEF

The DARPA Targeted Neuroplasticity Training (TNT) program is exploring ways to speed up skill acquisition by activating synaptic plasticity. If the program succeeds, downloadable learning that happens in a flash may be the result.

MINDHACK FOR FASTER LEARNING

In March 2016, DARPA — the U.S. military’s “mad science” branch — announced their Targeted Neuroplasticity Training(TNT) program. The TNT program aims to explore various safe neurostimulation methods for activating synaptic plasticity, which is the brain’s ability to alter the connecting points between neurons — a requirement for learning. DARPA hopes that building up that ability by subjecting the nervous system to a kind of workout regimen will enable the brain to learn more quickly.

[Taken]Military Researchers Are Hacking the Human Brain So We Can Learn Much Faster
Credit: DARPA

The ideal end benefit for this kind of breakthrough would be downloadable learning. Rather than needing to learn, for example, a new language through rigorous study and practice over a long period of time, we could basically “download” the knowledge after putting our minds into a highly receptive, neuroplastic state. Clearly, this kind of research would benefit anyone, but urgent military missions can succeed or fail based on the timing. In those situations, a faster way to train personnel would be a tremendous boon.

FIRST NEUROSTIMULATION, THEN APPLICATION

As part of the TNT program, DARPA is funding eight projects at seven institutions. All projects are part of a coordinated effort that will first study the fundamental science undergirding brain plasticity and will conclude with human trials. The first portion of the TNT program will work to unravel the neural mechanisms that allow nerve stimulation to influence brain plasticity. The second portion of the program will practically apply what has been learned in a variety of training exercises.

To ensure the work stays practical, foreign language specialists, intelligence analysts, and others who train personnel now will work with researchers to help refine the TNT platform to suit military training needs. Researchers will compare the efficacy of using an implanted device to stimulate the brain versus non-invasive stimulation. They will also explore both the ethics of enhanced learning through neurostimulation and ways to avoid side effects and potential risks.

The Evolution of Brain-Computer Interfaces [INFOGRAPHIC]
Click to View Full Infographic

“The Defense Department operates in a complex, interconnected world in which human skills such as communication and analysis are vital, and the Department has long pushed the frontiers of training to maximize those skills,” Doug Weber, the TNT Program Manager, said in a DARPA press release. “DARPA’s goal with TNT is to further enhance the most effective existing training methods so the men and women of our Armed Forces can operate at their full potential.”

If the TNT program succeeds, striving to be all you can be may mean learning at a much faster pace, and not just for military personnel. Downloadable learning may be one of the ways we achieve next-level humanity.

 

via DARPA Is Planning to Hack the Human Brain to Let Us “Upload” Skills

, , , , , , ,

Leave a comment

[ARTICLE] Brain Computer Interface issues on hand movement – Full Text

January 2018

Abstract

This paper focuses on the Brain Computer Interface (BCI) application and its issues. Further the attempt was made to implement left and right hand movement classification after removal of the artifacts in the acquired signals of the various hand movements.

 

1. Introduction

The Brain Computer Interface (BCI) involves a combination of the brain and device both sharing an interface to enable communication channel between the brain and an object that have to be controlled externally. The human brain has innumerable neurons which are connected to each other for transmission of impulses. As an electrode chip is implemented into the brain via surgical methodology the electrical signals produced by the neurons are transmitted to the computer which then translates the signals into data. These data are interpreted to control a computer device. In 2013, Lebedev successfully coupled the brains of two rats making use of an interface to enable direct sharing of information (Pais-Vieira et al., 2013). Minute fluctuations in voltages between neurons are measured and signals are amplified to produce graphs. While the Invasive BCIs focus on direct implementation into the grey matter of the brain to produce the highest quality of signals by neurosurgery, Non Invasive BCIs make use of techniques like Electroencephalography (EEG), Magneto Encephalography (MEG) and function Magnetic Resonance Imaging (fMRI). EEG techniques experience placing of electrodes on the scalp accompanied by a conductive gel or paste. Many systems are known to use electrodes which are attached to separate wires. Over the years, BCI has been instrumental in developing intelligent relaxation devices, providing enhanced control of devices like wheelchairs and vehicles, controlling robots and computer cursors and providing an additional channel of control in computer games. Bionic eyes have been known to restore sight for people having vision loss (Krishnaveni et al., 2012).

Considering the case of a motor imagery which refers to a mental process wherein an individual replicates an action. Thus, a mental representation of movement prevails without an actual body movement. Imagination efficiency is hard to control. Hence controlling EEG enables an individual to communicate despite the inability to control voluntary muscles. Interface substitute for nerves and muscles and the signals are incorporated into the hardware and software to be translated into physical actions. EEG based BCIs can record and classify EEG changes through different types of motor imagery like imagination of right and left hand and activity, consequently motor imagery as means to enhance motor function and motor learning. It has made a significant contribution in the field of neurological rehabilitation, cognitive neuroscience and cognitive psychology. Clinical applications have procured a great deal of aid from motor imagery ranging from enhancing mobility and locomotion to reduce neuropathic pain (Malouin and Richards, 2013). Analysis and interception of data are challenging as EEG signals are vulnerable to varying fluctuations often termed as noise. Various strategies have been devised for prevention and removal of noise. In this paper, we apply Butterworth filter mechanism to eliminate noise from the signals to enhance the data quality. Besides we concentrate on feature extraction to transform raw signals into informative signals. We make use of Support Vector Machine for the same. Feature extraction contributes significantly in image processing.

A step by step process involved in Brain Computer Interface system is shown in the Fig. 1. Signal is acquired through various means such as invasive (ECog, Neurosurgery) and Non-invasive (EEG, fMRI, MEG) techniques. The channel selection is one of the important considerations since most of the EEG channel represent redundant information (Sleight et al., 2009).
Process involved in brain computing interface system

Figure 1. Process involved in brain computing interface system.

Fig. 2 shows the EEG channel placement on the human scalp. Each scalp electrode is located at the brain centres. In 2001 Pfurtscheller (Wolpaw, 2002) identified that many of the neural activity related to fist movements are found in channels C3, C4 and Cz as shown in Fig. 2 B. F7 is for rational activities, Fz is for intentional and motivational data, P3, P4 and Pz contain perception and differentiation, T3, T4 is for emotional processes, T5, T6 has memory functions and O1 and O2 contain visualization data.

EEG channel placements on the human scalp (http://static

In order to remove the noise from the obtained signal, any of the suitable filtering techniques may be adopted. Further the extracted data may move for classification phase. […]

Continue —-> Brain Computer Interface issues on hand movement – ScienceDirect

, , , , , , , ,

Leave a comment

%d bloggers like this: