Posts Tagged Paralysis

[WEB PAGE] A dual-therapy approach to boost motor recovery after a stroke

by Ecole Polytechnique Federale de Lausanne

**A dual-therapy approach to boost motor recovery after a stroke
Credit: Ecole Polytechnique Federale de Lausanne

Paralysis of an arm and/or leg is one of the most common effects of a stroke. But thanks to research carried out by scientists at the Defitech Foundation Chair in Brain-Machine Interface and collaborators, stroke victims may soon be able to recover greater use of their paralyzed limbs. The scientists’ pioneering approach brings together two known types of therapies—a brain-computer interface (BCI) and functional electrical stimulation (FES) – and has been published in Nature Communications.

“The key is to stimulate the nerves of the paralyzed 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 reestablish the link between the two nerve pathways where the signal comes in and goes out,” says José del R. Millán, who holds the Defitech Chair at EPFL.

Twenty-seven patients aged 36 to 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 using electrodes. That let the scientists pinpoint exactly where the electrical activity occurred in the brain tissue when the patients tried to reach out their hands. Every time that 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.

Reactivated tissue

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. What’s more, the enhanced motor function didn’t seem to diminish with time. Evaluated again 6-12 months later, the patients hadn’t lost any of their recovered mobility.

Explore further Electrically stimulating the brain may restore movement after stroke

More information: A. Biasiucci et al, Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke, Nature Communications (2018). DOI: 10.1038/s41467-018-04673-z

Journal information: Nature Communications

Provided by Ecole Polytechnique Federale de Lausanne

, , , , , , , ,

Leave a comment

[ARTICLE] Functional Electrical Stimulation Therapy for Retraining Reaching and Grasping After Spinal Cord Injury and Stroke – Full Text

Neurological conditions like hemiplegia following stroke or tetraplegia following spinal cord injury, result in a massive compromise in motor function. Each of the two conditions can leave individuals dependent on caregivers for the rest of their lives. Once medically stable, rehabilitation is the main stay of treatment. This article will address rehabilitation of upper extremity function. It is long known that moving the affected limb is crucial to recovery following any kind of injury. Overtime, it has also been established that just moving the affected extremities does not suffice, and that the movements have to involve patient’s participation, be as close to physiologic movements as possible, and should ideally stimulate the entire neuromuscular circuitry involved in producing the desired movement. For over four decades now, functional electrical stimulation (FES) is being used to either replace or retrain function. The FES therapy discussed in this article has been used to retrain upper extremity function for over 15 years. Published data of pilot studies and randomized control trials show that FES therapy produces significant changes in arm and hand function. There are specific principles of the FES therapy as applied in our studies: (i) stimulation is applied using surface stimulation electrodes, (ii) there is minimum to virtually no pain during application, (iii) each session lasts no more than 45–60 min, (iv) the technology is quite robust and can make up for specificity to a certain extent, and (v) fine motor function like two finger precision grip can be trained (i.e., thumb and index finger tip to tip pinch). The FES therapy protocols can be successfully applied to individuals with paralysis resulting from stroke or spinal cord injury.


Application of functional electrical stimulation (FES) for therapeutic purposes in rehabilitation settings dates back to the 1960’s when Liberson et al. (1961) used an FES system to stimulate the peroneal nerve to correct foot drop by triggering a foot switch, a single-channel electrical stimulation device stimulated the common peroneal nerve via a surface electrode, producing ankle dorsiflexion during the swing phase of gait (Liberson et al., 1961). This led to the first commercially available FES system with surface stimulation electrodes. Since then FES technology has been researched extensively to evaluate its benefits in diverse neurological conditions, and using an array of application techniques (Baldi et al., 1998Field-Fote, 2001Popovic et al., 2005201120122016Yan et al., 2005Frotzler et al., 2008Griffin et al., 2009Daly et al., 2011Kapadia et al., 201120132014aGiangregorio et al., 2012Malešević et al., 2012Martin et al., 2012Kawashima et al., 2013Lee et al., 2013Sadowsky et al., 2013Ho et al., 2014Kapadia N. et al., 2014Popović, 2014Sharif et al., 2014Bauer et al., 2015Howlett et al., 2015Vafadar et al., 2015Buick et al., 2016Cuesta-Gómez et al., 2017Fu et al., 2019Straudi et al., 2020). The two common uses of FES are to replace function (i.e., as an orthotic device) and to retrain function (i.e., as a therapeutic device). In this article we will limit ourselves to the therapeutic application of FES.

In the therapeutic application (FES therapy), FES is used as a short-term treatment modality. The expectation is that, after training with the FES system, the patients will be able to voluntarily perform the trained activities without FES (i.e., patients are expected to regain voluntary function). To date, a few high-quality randomized controlled trials have been performed, proving the efficacy of FES therapy over other rehabilitation techniques (Sharififar et al., 2018Yen et al., 2019). This paucity in multicenter randomized controlled trials and the limited access to systems that can properly deliver FES therapy might have affected its uptake in clinical settings (Ho et al., 2014Auchstaetter et al., 2016). Fortunately, both these issues are being addressed as new FES systems that are specifically developed for FES therapy are being introduced, as well as large scale multicenter randomized controlled trials are being planned to further confirm the efficacy of this rehabilitation modality. This article will provide readers with the details on how transcutaneous multichannel FES therapy for the upper extremity can be applied in clinical trials and as such the same methodology can be used in clinical practice by physiotherapists and occupational therapists.

The FES methodology discussed here has been developed with the intent to be user friendly, robust and to be able to produce better functional gains than the presently available best-practice rehabilitation techniques. The FES system used in our laboratory is a surface stimulation system with up to 4 stimulation channels that can produce gross motor function as well as precision grips such as two finger pinch grip. However, the methodology of FES application discussed here is pertinent to any multichannel transcutaneous FES device. We have used transcutaneous FES to retrain reaching and grasping in individuals with both spinal cord injury and stroke (Thrasher et al., 2008Kapadia and Popovic, 2011Kapadia et al., 20112013Popovic et al., 2012Hebert et al., 2017). The results obtained in both patient populations indicate functional improvements after 8–14 weeks of therapy (20–48 h of stimulation). Patients showed reduced dependency on caregivers, and some even became independent in their activities of daily living.

This article will extensively detail how FES was applied in these previously successful clinical trials to retrain reaching and grasping functions in individuals who sustained a spinal cord injury or a stroke.[…]


, , , , , , , , ,

Leave a comment

[BLOG POST] Disabled Influencer Lina on Helping People Break Down Their Own Barriers

Lina Bettayeb wearing a beige hijab and grey top sat by a window with a vase containing pink and white flowers
Listen to this page using ReadSpeaker

Disability isn’t as well represented as it should be, but it’s even worse for minority groups within the disability community. We want to change that, so are interviewing a series of disabled activists and influencers from different minorities. Here, we speak to Lina Bettayeb – or ‘Lina the dreamer’, as she is known to her 5,000 Instagram followers – a 22-year-old disabled Muslim influencer living in London.

Please tell Disability Horizons readers who you are and what you do?

I would describe myself as an ambitious individual who does their best to overcome any challenges whilst also trying to see the positives of a situation.

My time is shared between studying for a neuroscience degree and trying to recover movement in my body after I was paralysed from the chest down in a car accident in 2014.

I love a good adventure and consider myself an avid adrenaline junkie. Reading in my garden has also recently become one of my favourite pastimes, along with painting and baking.

Lina Bettayeb wearing a brown hijab sat on the ground in front of a tree

You acquired a disability at a fairly young age. How did the world change for you, going from living as non-disabled to disabled?

I was 16 when I found myself in a differently-abled body. It was so surreal for so long. I even get moments today where it still feels unreal.

The fact that I know what life is like as a fully able-bodied person and a disabled person has opened my eyes to so much.

It is definitely harder when you’re not as independent and have to rely on people or things, such as ramps and elevators. There’s so much that I didn’t know about this world before my accident, and I’ve gained massive respect for anyone who deals with a physical challenge.

Did your ambitions and perceptions of life change when you became disabled?

I am naturally an ambitious person, but my drive definitely went to another level after I became paralysed. I wanted to challenge any limit or barrier that I had in my mind, or that anyone or society placed on what I ‘could’ achieve. If you don’t try, how will you ever know?

My perception of many things changed too. From my body image to family, and my relationship with God. It’s crazy how sometimes you have to feel the absence of something in order to notice its value.

With my injury and reliance on a wheelchair to get around, I’ve had to teach myself to be more patient as things take longer than before. I’m also more appreciative of what I have as it could be much worse.

I’ve become more vocal too as no one except myself knows what I’m going through and what my needs are. It’s made me more empathetic as I know what it’s like to be in a position of physical vulnerability that I never felt before.

What inspired you to become a social media influencer and how did get to where you are today?

It started around the middle of my hospital journey –  I was there for a whole year. During that time, I had started to document my feelings and progress to the people I knew.

Lina Bettayeb wearing a grey hijab looking over a hedge with only her eyes showing

It started with a blog, where I would share my therapy progress, as well as any thoughts I had. It was also a way for people to contact me and stay connected. I then branched it out to Instagram so that people could follow my experience and send support.

A few years after that, I had started a movement entitled ‘knowing your true ability’. I hoped that it would encourage others to overcome their challenges and learn and achieve from them.

That resonated with a lot of people and, ever since, that has been a goal of mine – to keep sharing and encouraging this mantra.

Have you ever encountered prejudice or discrimination from your followers or the general public?

Thankfully no. I’ve mostly received support and assistance. Outside, I tend to get a lot of curious stares and pity smiles, but as much as they make me feel uncomfortable, I know they’re not ill-intended.

Mental health is often a taboo topic, but as a disabled person have you ever gone through a period of internal struggle? If so, how did you overcome it?

The internal struggle is a daily companion. I’ve had to learn to be self-aware so that I can maintain an equilibrium in my mind.

This involves recognising any triggers or draining factors, including negativity and overuse of social media, and doing things that re-energise me, such as hobbies and speaking to God.

Training the way I think and my perception has also helped massively. I firmly believe that there are multiple ways to see a situation.

I try to ask: ‘Is it worth me stressing over? Is there a silver lining in this? What is this teaching me?’, instead of, ‘Why is this happening to me?’.

Keeping myself in check but also allowing some time to recharge is what works for me.

Who do you get influenced by and who motivates you?

I gain influence and motivation from so many different places. It could be a conversation with someone or a passage in a book. But to narrow it down to two sources, it would be the Prophet Muhammad (peace be upon him) for his tenacity and patience, and my mother for her perseverance and her love of God.

Lina Bettayeb wearing a grey hijab stood in front of a tree looking into the distance.

What is next for you both professionally and personally?

I would love to spread my message as far as I can and to as many people possible. This is what keeps me going every single day.

I’d love to work alongside other differently-abled individuals and celebrate our differences in a video.

By allowing everyone to tell their own stories, instead of having them told by someone else, I want people to really recognise the true warriors in the ‘disabled’ community. We are all more than just a label.

I am also working towards a personal dream I have had since being injured – to learn to fly a plane. I’m so fortunate to have found an organisation that can help me with working towards this with the right adaptations. I am now officially considered a student pilot.

What final message would you like to convey to people?

I hope that my experiences of living with physical paralysis can shine a light on the fact that we all face paralysis in one way or another. These might be physically or mentally through the barriers we place in our minds.

You might be paralysed by not believing in yourself, seeing the negative in everything, not being willing to try something out due to fear of failure or society saying you can’t.

These are all obstacles that can stop us from progressing. Imagine if we all lifted these from our minds – the potential we would reach is beyond worth it.

You can follow Lina on Instagram at linathedreamer.

Interview by Raya AlJadir


, , , , , , , ,

Leave a comment

[Webinars] Latest Reeve Foundation Webinar Series Focuses on Self-Care for Caregivers

Published on

The Christopher & Dana Reeve Foundation is hosting a three-part webinar series on self-care, managing anxiety, and mental health in December and January.

These webinars are produced as part of the Paralysis Resource Center’s community education initiative. The webinars are for those caring for a loved one or individuals living with paralysis.

Webinar 1 – The Art of Managing Anxiety and Worry: Behavioral Strategies will take place Tuesday, December 3, at 2 PM ET

Webinar 2 – Mental Health and Self-Care for Caregivers: Beyond Finding the Time will take place Thursday, December 12, at 2 PM ET

Webinar 3 – Using Mindfulness to Support Life After Paralysis: Strategies to Increase Mindfulness will take place Wednesday, January 8, at 2 PM ET.

“Taking time to find a self-care routine to help manage anxiety and stress and being mindful of your surroundings is an important step for quality of life and overall well-being,” says Angela Cantillon, Director of Operations, Paralysis Resource Center, in a media release from the Reeve Foundation.

“We think these webinars will help individuals find ways to reduce worrisome thoughts, find new techniques to navigate daily life, and encourage healthy habits.”

All three sessions will be hosted by Terry Gupta, MSW, C-IAYT, E-RYT500, YACEP, who, along with her partner Jay Gupta, RPh, MSc, MTM Specialist, C-IAYT, are co-Founders of and

Visit the Christopher & Dana Reeve Foundation for more information.

[Source(s): Christopher & Dana Reeve Foundation, PR Newswire]


via Latest Reeve Foundation Webinar Series Focuses on Self-Care for Caregivers – Rehab Managment

, , , ,

Leave a comment

[BLOG POST] PathMaker Neurosystems – Publication of First Clinical Trial Results for MyoRegulator® Device for Non-Invasive Treatment of Spasticity

PathMaker Neurosystems Inc. (“PathMaker”), a clinical-stage bioelectronic medicine company developing breakthrough non-invasive systems for the treatment of patients with spasticity and paralysis has announced the first publication of clinical trial results for its MyoRegulator® device for the non-invasive treatment of spasticity. Published in Bioelectronic Medicine, the results provide the first clinical evidence using MyoRegulator to treat upper extremity spasticity in subjects with chronic stroke. MyoRegulator is an investigational medical device and is limited by US Federal law to investigational use only.

Spasticity is a chronic condition characterised by painful muscle contractions and is common in patients suffering from stroke, cerebral palsy, multiple sclerosis, spinal cord injury, traumatic brain injury and other neurological disorders. Management of spasticity is a difficult challenge and is currently managed primarily by pharmacological agents and injected botulinum neurotoxins, and there is tremendous unmet medical need.  MyoRegulator is a first-in-class non-invasive device based on PathMaker’s proprietary DoubleStim™ technology (combining anodal trans-spinal direct current stimulation (tsDCS) and peripheral nerve direct current stimulation (pDCS)), which provides simultaneous non-invasive stimulation intended to suppress hyperexcitable spinal neurons involved with spasticity.

“Current pharmacological approaches to managing spasticity have, at best, short-term efficacy, are confounded by adverse effects, and are often unpleasant for the patient,” said co-author Zaghloul Ahmed, Ph.D., Professor and Chairman, Department of Physical Therapy and Professor, Center for Developmental Neuroscience, CUNY and Scientific Founder of PathMaker Neurosystems. “The initial study results demonstrate the potential of a novel, non-invasive treatment to reduce spasticity and improve functional recovery in patients with upper motor neuron syndrome after stroke.”

The publication, Non-Invasive Treatment of Patients with Upper Extremity Spasticity Following Stroke Using Paired Trans-spinal and Peripheral Direct Current Stimulation, was authored by researchers at Feinstein Institute for Medical Research at Northwell Health (Manhasset, NY) led by Bruce Volpe, M.D. The study included patients with upper limb hemiparesis and wrist spasticity at least 6 months after their initial stroke in a single-blind, sham-controlled, crossover design study to test whether MyoRegulator treatment reduces chronic upper-extremity spasticity.

Twenty subjects received five consecutive 20-minute daily treatments with sham stimulation followed by a 1-week washout period, then five consecutive 20-minute daily treatments with active stimulation. Subjects were told that the order of active or sham stimulation would be randomized. Clinical and objective measures of spasticity and motor function were collected before the first session of each condition (baseline), immediately following the last session of each condition, and weekly for 5 weeks after the completion of active treatments. The results demonstrated significant group mean reductions from baseline in both Modified Tardieu Scale scores (summed across the upper limb, P<0.05), and in objectively measured muscle resistance at the wrist flexor (P<0.05) following active treatment as compared to following sham treatment. Motor function also improved significantly (measured by the Fugl-Meyer and Wolf Motor Function Test; P<0.05 for both tests) after active treatment, even without additional prescribed activity or training. The effect of the active MyoRegulator treatment was durable for the 5-week follow-up period.

We are highly encouraged by these clinical results which demonstrate the potential of MyoRegulator to improve outcomes for patients suffering from spasticity, without the need for surgery or drugs. Building on these results and our ongoing clinical trial in Europe, we expect to initiate a US multi-center, pivotal, double-blind clinical trial supported by the National Institute of Neurological Disorders and Stroke (NINDS) in early 2020.

Nader Yaghoubi, M.D., Ph.D., President and Chief Executive Officer of PathMaker


About PathMaker Neurosystems Inc.

PathMaker Neurosystems is a clinical stage bioelectronic medicine company developing breakthrough non-invasive systems for the treatment of patients with chronic neuromotor conditions. With offices in Boston (US) and Paris (France), we are collaborating with world-class institutions to rapidly bring to market disruptive products for treating spasticity, paralysis and muscle weakness. In January 2019, we announced a collaboration and distribution agreement with WeHealth Digital Medicine to commercialise the MyoRegulator® device worldwide, excluding US and Japan territories retained by PathMaker.  More than 48 million patients in the US, Europe and China suffer disabilities due to stroke, cerebral palsy, multiple sclerosis, spinal cord injury, traumatic brain injury, Parkinson’s disease and other neurological disorders. At PathMaker, we are opening up a new era of non-invasive neurotherapy for patients suffering from chronic neuromotor conditions. For more information, please visit the company website at

Source: PathMaker Neurosystems Inc.

via PathMaker Neurosystems – Publication of First Clinical Trial Results for MyoRegulator® Device for Non-Invasive Treatment of Spasticity | ACNR | Online Neurology Journal

, , , , ,

Leave a comment

[WEB PAGE] Reconnecting the Disconnected: Restoring Movement in Paralyzed Limbs – Video

"Moving an arm can involve more than 50 different muscles," UA professor Andrew Fuglevand said. "Replicating how the brain naturally coordinates the activities of these muscles is extremely challenging."

“Moving an arm can involve more than 50 different muscles,” UA professor Andrew Fuglevand said. “Replicating how the brain naturally coordinates the activities of these muscles is extremely challenging.”

UA professor Andrew Fuglevand is using artificial intelligence to stimulate multiple muscles to elicit natural movement in ways previous methods have been unable to do.
Dec. 20, 2018

Andrew Fuglevand

Andrew Fuglevand

Scientists now know that the brain controls movement in people by signaling groups of neurons to tell the muscles when and where to move. Researchers also have learned it takes a complex orchestration of many signals to produce even seemingly simple body movements.

If any of these signals are blocked or broken, such as from a spinal cord injury or stroke, the messages from the brain to the muscles are unable to connect, causing paralysis. The person’s muscles are functional, but they no longer are being sent instructions.

Andrew Fuglevand, professor of physiology at the University of Arizona College of Medicine – Tucson and professor of neuroscience at the UA College of Science, has received a $1.2 million grant from the National Institutes of Health to study electrical stimulation of the muscles as a way to restore limb movements in paralyzed individuals. Fuglevand’s goal is to restore voluntary movement to a person’s own limbs rather than relying on external mechanical or robotic devices.

Producing a wide range of movements in paralyzed limbs has been unsuccessful so far because of the substantial challenges associated with identifying the patterns of muscle stimulation needed to elicit specified movements, Fuglevand explained.

“Moving a finger involves as many as 20 different muscles at a time. Moving an arm can involve more than 50 different muscles. They all work together in an intricate ‘dance’ to produce beautifully smooth movements,” he said. “Replicating how the brain naturally coordinates the activities of these muscles is extremely challenging.”

Recent advances in “machine learning,” or artificial intelligence, are making the impossible possible.

Fuglevand, who also is an affiliate professor of biomedical engineering and teaches neuroscience courses at the UA, is employing machine learning to mimic and replicate the patterns of brain activity that control groups of muscles. Tiny electrodes implanted in the muscles replay the artificially generated signals to produce complex movements.

“If successful, this approach would greatly expand the repertoire of motor behaviors available to paralyzed individuals,” he said.

“More than 5 million Americans are living with some form of paralysis, and the leading causes are stroke and spinal injury,” said Nicholas Delamere, head of the UA Department of Physiology. “New innovations in artificial intelligence, developed by scientists like Fuglevand and his team, are allowing them to decode subtle brain signals and make brain-machine interfaces that ultimately will help people move their limbs again.”

“The headway researchers have made in our understanding of artificial intelligence, machine learning and the brain is incredible,” said UA President Robert C. Robbins. “The opportunity to incorporate AI to brain-limb communication has life-changing potential, and while there are many challenges to optimize these interventions, we are really committed to making this step forward. I am incredibly excited to track Dr. Fuglevand’s progress with this new grant.”

Research reported in this release was supported by the National Institutes of Health, National Institute of Neurological Disorders and Stroke, under grant No. 1R01NS102259-01A1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
A version of this article originally appeared on the UA Health Sciences website:


via Reconnecting the Disconnected: Restoring Movement in Paralyzed Limbs | UANews

, , , , , , , ,

Leave a comment

[WEB SITE] Building a better brain-computer interface

Building a better brain-computer interface

Photo of a dummy BrainGate interface. Credit: Paul Wick/Wikimedia Commons

October 2, 2018 by Matt Miles, Medical Xpress

Brain-computer interfaces, or BCIs, represent relatively recent advances in neurotechnology that allow computer systems to interact directly with human or animal brains. This technology is particularly promising for use in cases of spinal cord injury or paralysis. In these situations, patients may be able to use neural decoders that access part of their brain to operate a prosthetic limb or even to re-animate a paralyzed limb through functional electrical stimulation (FES).

Michael A. Schwemmer and colleagues, in a recent Nature Medicine article, detail their research on BCIs using  decoders with a participant with tetraplegia due to spinal cord injury. Their research focuses on addressing several key needs identified by end-users of BCI systems, namely: high , minimal daily setup, rapid response time, and multifunctionality—all of which are characteristics heavily influenced by a BCI’s particular neural decoding algorithm.

Schwemmer’s group describes several different approaches to training and testing three variations on neural network decoders (NN-BCI) in comparison with each other and a benchmark support vector machine (SVM) decoder. The four BCI decoder paradigms were developed and tested over the course of several years in association with a 27-year-old male participant with tetraplegia. The participant had a 96-channel microelectrode array implanted in the area of his left primary motor cortex corresponding to the hand and arm. Using intracortical data collected from 80 sessions over 865 days, the investigators trained and evaluated these BCI decoders. These sessions consisted of two 104-second blocks of a four-movement task: index extension, index flexion, wrist extension, and wrist flexion.

The initial neural network (NN)  was developed and calibrated using data from the first 40 sessions (80 blocks); it was not updated over the second half of the training/testing period, and is referred to here as the fixed neural network (fNN) model. From the fNN, two other neural network models were created: a supervised updating (sNN) model and an unsupervised updating (uNN) model. Both models used data from the first block of the second 40-session (updating/testing) period. The sNN model’s algorithm relies on explicit training labels, that is, known timing and type of movement, whereas the uNN model relies on undifferentiated or unknown direct input in relation to intended action of the limb. The second block of the second 40-session period was used for accuracy testing of all models—fNN, sNN, uNN, and SVM.

The purpose of using four separate models here was to test and demonstrate various aspects of the three neural network models in relation to each other and the benchmark SVM model. For instance, the supervised neural network (sNN) model was updated daily (during the first block of the second 40-session period) and compared directly with the daily-retrained SVM model. The fixed neural network (fNN) model was provided to demonstrate that a BCI could sustain accuracy for over a year with no updates.

The unsupervised neural network (uNN) was perhaps the most interesting comparator, as we shall see, because it attempted to combine the improved accuracy gained from daily updates but without the consequent daily setup time required by the sNN model. Accuracy was the key performance measure in all tests, defined here as a percentage of correctly predicted time-bins in the second block of the second 40 sessions; the criterion of greater than 90% accuracy was one of the four end-user requirements originally articulated at the outset of the study.

The sNN consistently outperformed the daily-retrained SVM: in 37 out of 40 sessions, its accuracy was > 90%, whereas the SVM only achieved > 90% accuracy in 12 sessions. The fNN also outperformed the SVM in 36 of 40 sessions; it achieved > 90% accuracy in 32 sessions. The fNN accuracy was, not surprisingly, lower than the accuracy of the sNN, and both fixed decoders, fNN and SVM, declined in accuracy over the course of the study period, in contrast to the daily-updated decoders.

Perhaps the most interesting finding of this research however, is the performance of the unsupervised neural network (uNN), which outperformed both fixed models in terms of accuracy, while also meeting the end-user requirement of minimal daily set-up. Where the sNN model required explicit daily training, the uNN incorporated data from general use in its update schema, which required no such daily set-up. In comparison with the fNN, a performance gap emerged over time, and the benefits of the uNN distinguished themselves. The uNN also outperformed the SVM in terms of response time, another key end-user requirement.

Another important aspect of this study with regard to NNs focused on transfer learning, whereby new movements can be added to the existing repertoire with minimal additional training and data. In this case, “hand open” and “hand close” were added to the previous four movements, and all decoders were rebuilt. Here too, unsupervised updating was used to build an unsupervised transfer  (utNN), which, after only one session of training oupterformed the SVM model.

Finally, the previous research—all of which was conducted in an “offline” setting—was applied, via the participant’s FES-controlled hand and forearm, to show that a transfer learning uNN trained on the original four-movement task could be used to quickly create a new decoder to control, in real time, an open hand and three grips (can, fork, and peg). In a test of the system, the participant was able to perform all three hand movement grip tasks, with no failures, in 45 attempts. Previously, he was only able to perform one grip task successfully.

In summarizing how the results of their study relate to the main end-user expectations previously described, the investigators cite the following achievements: “(i) using deep NNs to create robust neural decoders that sustain high fidelity BCI control for more than a year without retraining; (ii) introducing a new updating procedure that can improve performance using data obtained through regular system use; (iii) extension of functionality through transfer learning using minimal additional data; and (iv) introducing a decoding framework that simultaneously addresses these four competing aspects of BCI performance (accuracy, speed, longevity, and multifunctionality). In addition, we provide a clinical demonstration that a decoder calibrated using historical data of imagined hand movements with no feedback can be successfully used in real-time to control FES-evoked grasp function for object manipulation.”

Schwemmer and colleagues go on to offer a more in-depth discussion of their results amidst the broader landscape of BCI research, and offer commentary on some of the specific challenges and limitations of their experiment. While noting that the median response time for uNN decoders (0.9 s) is still faster than that of SVM decoders (1.1 s), they acknowledge that a target of 750 ms or less is probably closer to realistic end-user expectations.

Ultimately they conclude: “We have demonstrated that decoders based on NNs may be superior to other implementations because new functions can be easily added after the initial decoder calibration using transfer learning. Crucially, we show that this secondary update to add more movements requires a minimal amount of additional data.” And “insights gained from offline data and analyses can carry over to a realistic online BCI scenario with minimal additional data collection.”

 Explore further: Using multi-task learning for low-latency speech translation

More information: Michael A. Schwemmer et al. Meeting brain–computer interface user performance expectations using a deep neural network decoding framework, Nature Medicine(2018). DOI: 10.1038/s41591-018-0171-y

via Building a better brain-computer interface

, , , , , ,

Leave a comment

[Thesis] Robotic rehabilitation of upper-limb after stroke – Implementation of rehabilitation control strategy on robotic manipulator – Full Text PDF

Globally, stroke is one of the main causes of permanent neurological damage \cite{WHO}. Partial or total paralysis of the extremities is the most common complication, with paralysis in upper-limbs being the most prevalent. Efficient and available rehabilitation therapy is essential for the patient’s recovery process. Traditional physical therapy is a resource intensive and commonly used approach. Research on robotic rehabilitation aims to provide a viable rehabilitation tool.The main objective of this master thesis is to design and implement a safe real-time control system on an industrial six-axis manipulator with an external force/torque sensor for robotic upper-limb rehabilitation of stroke patients.The chosen approach is to implement control strategies directly in the tool frame of the manipulator. This is achieved by utilizing the UR5 from Universal Robot and the Mini45 F/T sensor from ATI Industrial Automation. Two verification test are chosen based on activities of daily life (ADL). The best low-level control strategy is achieved by indirect force and torque control through a joint velocity interface.The UR5 firmware operates with an unknown internal controller. An external controller is designed incrementally to investigate the unknown system dynamics and find the best possible low-level performance. Numerous safety mechanisms are added to the external controller. Four high-level control strategies are developed and implemented.Three main safety-related challenges with robotic rehabilitation are identified. Two of them are related to and solved by the external force/torque sensor. The third challenge is related to the self-collisions inside the workspace of the UR5 manipulator. This challenge is also applicable to all six-axis robot manipulators. The three challenges are analyzed and solved with a safety-oriented approach.The safety and functionality of the robotic rehabilitation system are experimentally verified. The behaviour of the rehabilitation modes is analyzed and discussed based on raw data and video recordings.The conclusion is that robotic upper-limb rehabilitation of stroke patients utilizing the UR5 manipulator and the Mini45 F/T sensor is safe and feasible.

via Robotic rehabilitation of upper-limb after stroke – Implementation of rehabilitation control strategy on robotic manipulator

, , , , , , ,

Leave a comment

[WEB PAGE] What are the various treatment options for paralysis?

When there is a loss of muscular functioning in an area or sensory loss on area resulting usually from any damage to central nervous system, there is paralysis. Some of the probable causes of this dangerous condition are polio, stroke, excessive trauma or multiple sclerosis, etc. There may be complete paralysis or partial paralysis. It is mainly of two kinds, namely, paraplegia and quadriplegia. Paralysis is the consequence when the brain fails to send signals to various regions of the body. This may result from a variety of reasons. Stroke accounts for 30% of paralysis cases and is the major cause. However, one can choose paralysis treatment depending on the severity of the condition and the region which is paralyzed.

How is paralysis diagnosed?

On the event of any failure of muscular functioning or sensory loss on certain area, it is important to visit a medical practitioner immediately. To diagnose the condition, he prescribes a series of tests including CT Scan, MRI, X-Ray, Electromyography. If at all it is necessary, the patient may be suggested a neurologist. After paralysis is confirmed, the treatment begins. Certain types of paralysis may be cured and this mainly includes partial paralysis. You can ask the doctor whether the recovery is possible or not. No matter what the cause of the condition, the treatment procedure will be almost the same. Whatever treatment you choose for recovery, the treatment provider will try and restore brain and body connection. This is the only way to bring about recovery.

Some of the basic treatment options for paralysis

Wearable device running on electricity is the most basic treatment for paralysis. This wearable electronic device is also used for stroke treatment. It improves arm functioning and restores motion in the arms. When you wear this device, it delivers electrical current to activate the muscles of arms and legs. This technique of motion restoration is also termed as FES or Functional Electrical Stimulation. It can recover the feet or lower legs from paralysis. The use of FES along with specific exercises can bring about a relief.

Some of the best treatment options for paralysis 

If anyone of your loved one is suffering from paralysis, read the following section to learn how to reduce the symptoms:

  • Surgery can address physical barriers. It may be that there is an object fixed in the brain or spinal cord of the person. It needs to be got rid of. Through the surgery, certain portions of the spinal cord can also be fused together.
  • Some paralysis medication may be used to reduce swelling, inflammation and infection on the area. If there is chronic pain, it may be addressed with medicines.
  • Continuous monitoring of the person is mandatory to ensure that this condition does not get worse
  • Psychotherapy can help a lot. Support groups may teach you how to cope with this critical situation.
  • To restore muscular and nerve functioning, you may be asked to do certain exercises. Occupational therapy can also help a lot. Work on the injuries and practice them as much as possible. Physical therapy may reverse paralysis by rewiring the brain.
  • Some people got great results from alternative treatments like chiropractic care, massage therapy and acupuncture treatment.

If there are breathing difficulties, problem in the bowel movement, take immediate treatment for them. Again, surgery is an effective sleep apnea treatment. Whether it is sleep apnea or paralysis, immediate medical attention is required.

via What are the various treatment options for paralysis?- The New Indian Express

, , , ,

Leave a comment

[Abstract + References] Classifying Imaginary Hand Movement through Electroencephalograph Signal for Neuro-rehabilitation


Brain-Computer-Interface (BCI) has been widely used in the field of neuro-rehabilitation such as automatic controls based on brain commands to upper and lower extremity prosthesis devices in patients with paralysis. In a post-stroke period, approximately 50% of stroke sufferers have unilateral motor deficits leading to a chronic decline in chronic upper extremity function. Stroke affects patients in their productive and elderly age which is potentially creating new problems in national health development. BCI can be used to aid post-stroke patient recovery, thus motion detection and classification is essential for optimizing BCI device control. Therefore, this study aims to distinguish several hand functions such as grasping, pinching, and hand lifting from releasing movement in accordance with the usual movements performed during post-stroke rehabilitation based on brain signals obtained from electroencephalogram (EEG). In this study, the information that obtained from the processing of EEG signals were be used as inputs for artificial neural networks then classified to distinguish two types of imaginary hand movements (grasping v. releasing, pinching v. releasing, hand lifting v. releasing). The results of these classifications using Extreme Learning Machine (ELM) based on spectral analysis and CSP (Common Spatial Pattern) calculation show that ELM and CSP was a good feature in distinguishing two types of motion with software/system accuracy average above 95%. This could be useful for optimizing BCI devices in neuro-rehabilitation, such as combining with Functional Electrical Stimulator (FES) device as a self-therapy for post-stroke patient.


Badan Penelitian dan Pengembangan Kesehatan. Riset Kesehatan Dasar 2013, Available at :, accesed February 2017.

J. A. Franck. Concise Arm and Hand Rehabilitation Approach in Stroke. vol. 3. no. 4. 2015.

N. Birbaumer. A. R. Murguialday. and L. Cohen. Brain-computer interface in paralysis. Curr. Opin. Neurol. vol. 21. no. 6. pp. 634–8. 2008.

J. J. Daly. R. Cheng. J. Rogers. K. Litinas. K. Hrovat. and M. Dohring. Feasibility of a New Application of Noninvasive Brain Computer Interface (BCI): A Case Study of Training for Recovery of Volitional Motor Control After Stroke. J. Neurol. Phys. Ther. vol. 33. no. 4. pp. 203–211. 2009.

K. K. Ang. C. Guan. K. S. Phua. C. Wang. L. Zhou. K. Y. Tang. G. J. Ephraim Joseph. C. W. K. Kuah. and K. S. G. Chua. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke.. Front. Neuroeng. vol. 7. no. July. p. 30. 2014.

E. Buch. C. Weber. L. G. Cohen. C. Braun. M. A. Dimyan. T. Ard. J. Mellinger. A. Caria. S. Soekadar. A. Fourkas. and N. Birbaumer. Think to move: A neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke. vol. 39. no. 3. pp. 910–917. 2008.

G.-B. Huang. Q. Zhu. C. Siew. G. H. Ã. Q. Zhu. C. Siew. G.-B. Huang. Q. Zhu. and C. Siew. Extreme learning machine: Theory and applications. Neurocomputing. vol. 70. no. 1–3. pp. 489–501. 2006.

Emotiv Insight User Manual. 2015, Availabe at :, accessed June 2017

P. Szachewicz. Classification of Motor Imagery for Brain-Computer Interfaces. p. 50. 2013.

B. Shoelson. edfRead, Available at : 31900-edfread, accesed February 2017.

J. Ethridge and W. Weaver. Common Spatial Patterns Alogarithm. MatlabCentral. 2009. .

Q. Yuan. W. Zhou. S. Li. and D. Cai. Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res. vol. 96. no. 1–2. pp. 29–38. 2011.

G. Huang. Introduction to Extreme Learning Machines. Hands-on Work. Mach. Learn. Biomed. Informatics 2006. 2006.

M. H.. A. Samaha. and K. AlKamha. Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning. Int. J. Adv. Comput. Sci. Appl. vol. 4. no. 6. p. 6. 2013.

G. Lange. C. Y. Low. K. Johar. F. A. Hanapiah. and F. Kamaruzaman. Classification of Electroencephalogram Data from Hand Grasp and Release Movements for BCI Controlled Prosthesis. Procedia Technol. vol. 26. pp. 374–381. 2016.

X. Yong and C. Menon. EEG classification of different imaginary movements within the same limb. PLoS One. vol. 10. no. 4. pp. 1–24. 2015.

via Classifying Imaginary Hand Movement through Electroencephalograph Signal for Neuro-rehabilitation | Rahma | Walailak Journal of Science and Technology (WJST)

, , , , , , , , , ,

Leave a comment

%d bloggers like this: