Archive for category Functional Electrical Stimulation (FES)

[ARTICLE] Brain-Machine Neurofeedback: Robotics or Electrical Stimulation? – Full Text

Neurotechnology such as brain-machine interfaces (BMI) are currently being investigated as training devices for neurorehabilitation, when active movements are no longer possible. When the hand is paralyzed following a stroke for example, a robotic orthosis, functional electrical stimulation (FES) or their combination may provide movement assistance; i.e., the corresponding sensory and proprioceptive neurofeedback is given contingent to the movement intention or imagination, thereby closing the sensorimotor loop. Controlling these devices may be challenging or even frustrating. Direct comparisons between these two feedback modalities (robotics vs. FES) with regard to the workload they pose for the user are, however, missing. Twenty healthy subjects controlled a BMI by kinesthetic motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the EEG beta frequency-band (17–21 Hz) was turned into passive opening of the contralateral hand by a robotic orthosis or FES in a randomized, cross-over block design. Mental demand, physical demand, temporal demand, performance, effort, and frustration level were captured with the NASA Task Load Index (NASA-TLX) questionnaire by comparing these workload components to each other (weights), evaluating them individually (ratings), and estimating the respective combinations (adjusted workload ratings). The findings were compared to the task-related aspects of active hand movement with EMG feedback. Furthermore, both feedback modalities were compared with regard to their BMI performance. Robotic and FES feedback had similar workloads when weighting and rating the different components. For both robotics and FES, mental demand was the most relevant component, and higher than during active movement with EMG feedback. The FES task led to significantly more physical (p = 0.0368) and less temporal demand (p = 0.0403) than the robotic task in the adjusted workload ratings. Notably, the FES task showed a physical demand 2.67 times closer to the EMG task, but a mental demand 6.79 times closer to the robotic task. On average, significantly more onsets were reached during the robotic as compared to the FES task (17.22 onsets, SD = 3.02 vs. 16.46, SD = 2.94 out of 20 opportunities; p = 0.016), even though there were no significant differences between the BMI classification accuracies of the conditions (p = 0.806; CI = −0.027 to −0.034). These findings may inform the design of neurorehabilitation interfaces toward human-centered hardware for a more natural bidirectional interaction and acceptance by the user.

Introduction

About half of all severely affected stroke survivors remain with persistent motor deficits in the chronic disease stage despite therapeutic interventions on the basis of the current standard of care (Winters et al., 2015). Since these patients cannot use the affected hand for activities of daily living, novel interventions investigate different neurotechnological devices to facilitate upper limb motor rehabilitation, such as brain-machine interfaces (BMI), robotic orthoses, neuromuscular functional electrical stimulation (FES), and brain stimulation (Coscia et al., 2019). BMI approaches, for example, aim at closing the impaired sensorimotor loop in severe chronic stroke patients. They use robotic orthoses (Ang et al., 2015Kasashima-Shindo et al., 2015Belardinelli et al., 2017), FES devices (Kim et al., 2016Biasiucci et al., 2018), and their combination (Grimm et al., 2016cResquín et al., 2017) to provide natural sensory and proprioceptive neurofeedback during movement intention or imagery. It is hypothesized that this approach will lead to reorganization of the corticospinal network through repetitive practice, and might ultimately restore the lost motor function (Naros and Gharabaghi, 20152017Belardinelli et al., 2017Guggenberger et al., 2018).

However, these novel approaches often result in no relevant clinical improvements in severe chronic stroke patients yet (Coscia et al., 2019). Therefore, recent research has taken a refined and rather mechanistic approach, e.g., by targeting physiologically grounded and clinically relevant biomarkers with BMI neurofeedback; this has led to the conceptional differentiation between restorative therapeutic BMIs on the one side (as those applied in this study) and classical assistive BMIs on the other side like those applied to control devices such as wheel-chairs (Gharabaghi, 2016): While assistive BMIs intend to maximize the decoding accuracy, restorative BMIs want to enhance behaviorally relevant biomarkers. Specifically, brain oscillations in the beta frequency band have been suggested as potential candidate markers and therapeutic targets for technology-assisted stroke rehabilitation with restorative BMIs (Naros and Gharabaghi, 20152017Belardinelli et al., 2017), since they are known to enhance signal propagation in the motor system and to determine the input-output ratio of corticospinal excitability in a frequency- and phase-specific way (Raco et al., 2016Khademi et al., 20182019Naros et al., 2019).

However, these restorative BMI devices differ from their predecessors, i.e., assistive BMIs, by an intentionally regularized and restricted feature space, e.g., by using the beta frequency band as a feedback signal for BMI control (Gharabaghi, 2016Bauer and Gharabaghi, 2017). Such a more specific approach is inherently different from previous more flexible algorithms that select and weight brain signal features to maximize the decoding accuracy of the applied technology; restorative BMIs like the those applied in this study have, therefore, relevantly less classification accuracy than classical assistive BMIs (Vidaurre et al., 2011Bryan et al., 2013). As the regularized and restricted feature space of such restorative BMI devices leads to a lower classification accuracy in comparison to more flexible approaches, it may be frustrating even for healthy participants (Fels et al., 2015). IN the context of the present study, we conjectured that such challenging tasks will increase the relevance of extraneous load aspects like the workload (Schnotz and Kürschner, 2007). Furthermore, the modulation range of the oscillatory beta frequency band is compromised in stroke patients, proportionally to their motor impairment level (Rossiter et al., 2014Shiner et al., 2015). That means that more severely affected patients show less oscillatory event-related desynchronization (ERD) and synchronization (ERS) during motor execution or imagery (Pfurtscheller and Lopes da Silva, 1999). To our understanding, this underlines the relevance of beta oscillations as a therapeutic target for post-stroke rehabilitation. At the same time, however, this poses a major challenge for the affected patients and may, thereby, compromise their therapeutic benefit (Gomez-Rodriguez et al., 2011a,bBrauchle et al., 2015).

To overcome these hurdles that are inherent to restorative BMI devices, we have investigated different approaches in the past: (i) Reducing the brain signal attenuation by the skull via the application of epidural interfaces (Gharabaghi et al., 2014b,cSpüler et al., 2014), (ii) Augmenting the afferent feedback of the robotic orthosis by providing concurrent virtual reality input (Grimm et al., 2016a,b), (iii) combining the orthosis-assisted movements with neuromuscular (Grimm and Gharabaghi, 2016Grimm et al., 2016c) or transcranial electrical stimulation (Naros et al., 2016a) to enhance the cortical modulation range (Reynolds et al., 2015), and (iv) optimizing the mental workload related to the use of BMI devices.

In this study, we focus on the latter approach, i.e., optimizing the mental workload related to the use of BMI devices. For the latter approach it would be necessary to better understand the workloads related to different technologies applied in the context of BMI feedback (robotics vs. FES). We, therefore, investigated the mental demand, physical demand, temporal demand, performance, effort, and frustration of healthy subjects controlling a BMI by motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the beta frequency-band was turned into passive opening of the contralateral hand by a robotic exoskeleton or FES in a randomized, cross-over block design. The respective workloads were compared to the task-related aspects of active hand movement with EMG feedback. We conjectured a feedback-specific workload profile that would be informative for more personalized future BMI approaches.

Methods

Subjects

We recruited 20 healthy subjects (age = 23.5 ± 1.08 yeas [mean ± SD], range 19–27, 15 female) for this study. Subjects were not naive to the tasks. All were right-handed and reached a score equal or above 60 in the Edinburgh Handedness Inventory (Oldfield, 1971). The subjects gave their written informed consent before participation and the study protocol was approved by the Ethics Committee of the Medical Faculty of the University of Tübingen. They received monetary compensation.

Subject Preparation

We used Ag/AgCl electrodes in a 32 channel setup according to the international 10-20 system (Fp1, Fp2, F3, Fz, F4, FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, TP9, CP5, CP3, CP1, CPz, CP2, CP4, CP6, P3, Pz, P4, O1, O2 with TP10 as Reference and AFz as Ground) to examine the cortical activation pattern during the training session. Electrode impedances were set below 10 kΩ. All signals are digitalized at a sampling frequency of 1,000 Hz (robotic block) or 5,000 Hz (FES block) using Brain Products Amplifiers and transmitted online to BCI2000 software. BCI2000 controlled in combination with a custom-made software the respective feedback device, i.e., either the robotic orthosis or the functional electrical stimulation. Depending on the task, one of the following preparations was performed. Either the robotic hand orthosis (Amadeo, Tyromotion) was attached to the subject’s left hand (Figure 1A), fixated with Velcro strips across the forearm and with magnetic pads on the fingertips (Gharabaghi et al., 2014aNaros et al., 2016b); or functional electrical stimulation (FES, Figure 1B) was applied to the M. extensor digitorum communis (EDC) by the RehaMove2 (Hasomed GmbH, Magdeburg) with two self-adhering electrodes (50 mm, HAN-SEN Trading & Consulting GmbH, Hamburg). First an electrode was fixed to the distal end of the EDC’s muscle belly serving as ground. Then a rectangular electrode prepared with contact gel was used to find the optimal place for the second electrode where maximal extension of the left hand could be achieved. Here a custom written Matlab script was executed to detect the current threshold needed for the extension. Starting at 1 mA, the current was increased in steps of 0.5–1 mA. During each trial, FES was applied for 3 s with a pulse width of 1,000 μs and a frequency of 100 Hz. At the beginning of stimulation, a ramping protocol was implemented for 500 ms. Once, the correct position and threshold of stimulation were found, the temporary electrode was replaced by the second stimulation electrode and both were fixed with tape. A mean stimulation intensity of 6.5 mA (SD = 2.27) was required to cause the desired contraction in this study.

Figure 1. Experimental set-up. (Left) Robotic hand orthosis as feedback device (Amadeo, Tyromotion GmbH, Graz). (Middle) Neuromuscular forearm stimulation as feedback device (RehaMove 2, Hasomed GmbH, Magdeburg). In both cases, a brain-machine interface (BMI) detected motor imagery-related oscillations in the beta frequency band by an electroencephalogram (EEG) and provided via a BCI2000-system contingent feedback by moving the hand with either the robot or the electrical stimulation. (Right) The EEG montage used in this study.

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[Abstract] Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application

Highlights

• BCI methods are among the most effective tool for designing rehabilitation systems

.• Use of virtual reality (VR) can increase the efficiency of BCI rehab systems

.• “FES,” “Robotics Assistance,” and “Hybrid VR based Models” are main BCI approaches.

• In the future, flexible electronics can be used for designing stroke rehab systems.

Abstract

Strokes are a growing cause of mortality and many stroke survivors suffer from motor impairment as well as other types of disabilities in their daily life activities. To treat these sequelae, motor imagery (MI) based brain-computer interface (BCI) systems have shown potential to serve as an effective neurorehabilitation tool for post-stroke rehabilitation therapy. In this review, different MI-BCI based strategies, including “Functional Electric Stimulation, Robotics Assistance and Hybrid Virtual Reality based Models,” have been comprehensively reported for upper-limb neurorehabilitation. Each of these approaches have been presented to illustrate the in-depth advantages and challenges of the respective BCI systems. Additionally, the current state-of-the-art and main concerns regarding BCI based post-stroke neurorehabilitation devices have also been discussed. Finally, recommendations for future developments have been proposed while discussing the BCI neurorehabilitation systems.

Source: https://www.sciencedirect.com/science/article/abs/pii/S0010482520302031?dgcid=rss_sd_all&utm_campaign=RESR_MRKT_Researcher_inbound&utm_medium=referral&utm_source=researcher_app

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[VIDEO] Sensory Electrical Stimulation Glove for Hand Rehab After Stroke – SaeboStim Micro Unboxing Video –YouTube

Unbox the SaeboStim Micro with Dr. Scott Thompson. Boost your hand therapy with this electrical stimulation glove that improves function, weakness, and spasticity.

Want to learn more about electrical stimulation? Check out these FREE resources: https://www.saebo.com/blog/combining-…

https://www.saebo.com/blog/saebostim-…

https://www.saebo.com/blog/guide-elec…

For more information on the SaeboStim Micro:

https://www.saebo.com/shop/saebostim-…

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Download your FREE Saebo Exercise Guide here:

https://www.saebo.com/stroke-exercise…

Saebo, Inc. is a medical device company primarily engaged in the discovery, development, and commercialization of affordable and novel clinical solutions designed to improve mobility and function in individuals suffering from neurological and orthopedic conditions. With a vast network of Saebo-trained clinicians spanning six continents, Saebo has helped over 500,000 clients around the globe achieve a new level of independence.

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[WEB SITE] New technology is helping patients to get moving again – News

A ground-breaking ’bicycle’ which simulates muscle movements is helping a range of patients with long-term mobility problems caused by head or spinal injuries, stroke or MS. Julie Blackburn watched a demonstration.

One morning in April last year Jason Moffatt from Peel woke up with a headache.

And not just any normal headache, as he recalls: ’I don’t usually do headaches and this one was the worst: it felt like my head was about to explode out of the top.’

He put up with it for a while then decided it ’might be worth popping into the A&E’. It was lucky he did because an examination and subsequent scan revealed dried blood on his brain. He had suffered a bleed.

Jason was flown off the island to Walton Hospital in Liverpool for an operation but during surgery he suffered a stroke which left him paralysed down the left side of his body.

’I then spent three months in Liverpool, learning to walk again and do everyday tasks,’ he says.

While there, Jason realised that strokes do not just happen to older people, but to plenty of younger ones too.

Back on the island his rehabilitation programme has included sessions on a Functional Electrical Stimulation (FES) bicycle.

FES is a technique that uses low energy electrical pulses and has been found to be effective in restoring voluntary functions.

These pulses artificially generate body movements in specific muscle groups through electrodes placed on the patient’s body.

Jason’s physiotherapist is Christine Wright, from the Community Adult Therapy Services team. She specialises in helping patients with long-term neurological conditions and she demonstrated how the machine works.

Once the electrodes are positioned on the muscle groups which Jason needs to get working, he sits in a chair which is attached to the machine with his legs strapped onto the ’pedals’.

His session starts with a warm-up of around one and a half minutes before the resistance increases and he is working hard, concentrating on putting in more effort on his left leg.

Having started his treatments with around 10 to 15 minutes on the bike, Jason has now built up to 30 minutes in each session.

’I’ll be sweating at the end of this,’ he says.

As she keeps an eye on his progress, Christine explains: ’Although it’s a bike, the pattern of movement is simulating walking: each turn of the bike gives Jason a step.

’Numbers of repetitions lead to changes in the brain and the development of new neural pathways.

’The bike also strengthens the muscles so that, when those connections in the brain reform, those muscles are there, ready to be used.’

It has probably served Jason well that he was a keen cyclist before he became ill, having done the End2End mountain bike race, as well as the Parish Walk to Peel and the End to End walk.

He knows that he is also fortunate to have the use of the FES bicycle. When he was doing rehab in Liverpool, at a large, dedicated 30-bed rehab centre there, they didn’t have one: ’It was basically just a gym,’ he recalls. This is true of most rehab units where FES simulators are not part of the standard kit.

’We’re incredibly lucky to have this,’ Christine says.

This machine was purchased for the Community Physiotherapy Department two years ago with £11,695 provided by the Henry Bloom Noble Healthcare Trust.

The Trust’s main remit is to provide equipment over and above what the DHSC in the island would be able to buy.

It has been a great success for Christine and the other physiotherapists, Graihagh Betteridge and Rosie Callow, who are also trained to use the machine.

As well as working on patients’ lower limbs, the simulator can be detached from the bicycle element and used as a portable machine.

It can then be taken to people’s homes and used to help them regain shoulder and arm movement.

At the moment the department has to ration the machine’s use.

They take around 25 to 30 patients at a time, usually for a six-eight week course, with a session once a week on the bike.

They have a waiting list, both with new patients and patients who have had a course already and need further treatment. Because of this the Henry Bloom Noble Healthcare Trust has agreed to purchase a second bicycle so more patients will have the chance to use one.

Chairman of the Trust, Terry Groves, said: ’Jason’s story, and many others, have shown the value of this FES bicycle in managing differing conditions and rehabilitation.

’Recognising the continuing donations made to our Healthcare Trust we are delighted to fund the acquisition of this second FES bicycle from our funds so that continuing strides in this important area of aftercare can be made.’

Jason himself is delighted with the progress he has made using the bicycle: ’I can see an improvement. I can walk further and with a better balance,’ he says.

His aim now is to get back on his (real) bike.

Christine smiles when he says this. ’You will do it,’ she assures him.

via New technology is helping patients to get moving again | News |

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[VIDEO] Stroke Rehabilitation: Functional Electrical Stimulation (FES) for grasp and release – YouTube

Stroke Rehab ideas for incorporating your electrical stimulation (SaeboStim Pro) device in practicing grasp and release with your affected arm and hand. Home therapy series from Saebo UK

 

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[Book Chapter] A Sensorimotor Rhythm-Based Brain–Computer Interface Controlled Functional Electrical Stimulation for Handgrasp Rehabilitation. (Abstract + References)

Abstract

Each year, 795,000 stroke patients suffer a new or recurrent stroke and 235,000 severe traumatic brain injuries (TBIs) occur in the US. These patients are susceptible to a combination of significant motor, sensory, and cognitive deficits, and it becomes difficult or impossible for them to perform activities of daily living due to residual functional impairments. Recently, sensorimotor rhythm (SMR)-based brain–computer interface (BCI)-controlled functional electrical stimulation (FES) has been studied for restoration and rehabilitation of motor deficits. To provide future neuroergonomists with the limitations of current BCI-controlled FES research, this chapter presents the state-of-the-art SMR-based BCI-controlled FES technologies, such as current motor imagery (MI) training procedures and guidelines, an EEG-channel montage used to decode MI features, and brain features evoked by MI.

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via A Sensorimotor Rhythm-Based Brain–Computer Interface Controlled Functional Electrical Stimulation for Handgrasp Rehabilitation | SpringerLink

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[Abstract] An Omnidirectional Assistive Platform Integrated With Functional Electrical Stimulation for Gait Rehabilitation: A Case Study

Abstract

This paper presents a novel omnidirectional platform for gait rehabilitation of people with hemiparesis after stroke. The mobile platform, henceforth the “walker”, allows unobstructed pelvic motion during walking, helps the user maintain balance and prevents falls. The system aids mobility actively by combining three types of therapeutic intervention: forward propulsion of the pelvis, controlled body weight support, and functional electrical stimulation (FES) for compensation of deficits in angular motion of the joints. FES is controlled using gait data extracted from a set of inertial measurement units (IMUs) worn by the user. The resulting closed-loop FES system synchronizes stimulation with the gait cycle phases and automatically adapts to the variations in muscle activation caused by changes in residual muscle activity and spasticity. A pilot study was conducted to determine the potential outcomes of the different interventions. One chronic stroke survivor underwent five sessions of gait training, each one involving a total of 30 minutes using the walker and FES system. The patient initially exhibited severe anomalies in joint angle trajectories on both the paretic and the non-paretic side. With training, the patient showed progressive increase in cadence and self-selected gait speed, along with consistent decrease in double-support time. FES helped correct the paretic foot angle during swing phase, and likely was a factor in observed improvements in temporal gait symmetry. Although the experiments showed favorable changes in the paretic trajectories, they also highlighted the need for intervention on the non-paretic side.

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[Abstract] An EMG-controlled system combining FES and a soft exoskeleton glove for hand rehabilitation of stroke patients – Conference paper

In this paper, we present the development of a hybrid system which supports an active rehabilitation of the closing and the opening of the hand. The particularity of this system is to combine a soft exoskeleton glove, the SEM Glove™, and functional electrical stimulations (FES) to perform both types of hand movements. The created system is also a suggestion of improvement for the SEM Glove™ that is already commercialized by the BIOSERVO company and usable for hand closing rehabilitation only. In our study, a FES system was associated to this glove in order to provide the missing hand opening rehabilitation. To engage the patient in his rehabilitation, our system is electromyogram (EMG)-controlled and is activated according to the patient movement intentions. EMG signals of the muscles involved in the extension and flexion of the fingers were recorded and then processed in order to detect muscle activations. The control of the different elements of the system was then executed based on the results of this detection. The preliminary results demonstrated that the designed hybrid system shows good performances in detecting correctly the intention of a healthy user. Some improvements could still be made in the signal processing to increase the sensitivity of detection, but we proved that the hybrid system is already operational to assist the hand movements of a healthy user.

via An EMG-controlled system combining FES and a soft exoskeleton glove for hand rehabilitation of stroke patients – Danish National Research Database

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[VIDEO] Functional electrical stimulation (FES) for stroke patient to improve walking ability. – YouTube

Functional electrical stimulation (FES) for Walking its important training for Improvement the waking in Stroke patient, FES is guide training for patient foot to pick up from ground. It’s also showed improvement in walking after training with FES. I am thankful to my patient for giving me consent for this Video. I am want to thank MGM MCRI Hospital and MGM Physiotherapy Rehabilitation and Fitness Centre, Aurangabad, Maharashtra for constant support. Neuro Physiotherapist: Dr. Gaurav C. Mhaske (PT)

via Functional electrical stimulation (FES) for stroke patient to improve walking ability. – YouTube

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[VIDEO] Stroke Rehabilitation: Use of electrical stimulation to help the fingers be able to open and close – YouTube

 

This video demonstrates how to use FES, Functional Electrical Stimulation, to engage the muscles of the arm to extend and flex the fingers

via Stroke Rehabilitation: Use of electrical stimulation to help the fingers be able to open and close – YouTube

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