Posts Tagged Brain Computer Interface

[Abstract] Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor


It is essential to make sure patients be actively involved in motor training using robot-assisted rehabilitation to achieve better rehabilitation outcomes. This paper introduces an attention-controlled wrist rehabilitation method using a low-cost EEG sensor. Active rehabilitation training is realized using a threshold of the attention level measured by the low-cost EEG sensor as a switch for a flexible wrist exoskeleton assisting wrist ?exion/extension and radial/ulnar deviation. We present a prototype implementation of this active training method and provide a preliminary evaluation. The feasibility of the attention-based control was proven with the overall actuation success rate of 95%. The experimental results also proved that the visual guidance was helpful for the users to concentrate on the wrist rehabilitation training; two types of visual guidance, namely looking at the hand motion shown on a video and looking at the user’s own hand, had no significant performance difference; a general threshold of a certain group of users can be utilized in the wrist robot control rather than a customized threshold to simplify the procedure.

via Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor – IEEE Journals & Magazine

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[NEWS] The brain-computer interface at UCLA – from the 1970s to today

Apr 19, 2019 By UCLA Samueli Newsroom

In 1973, UCLA computer science professor Jacques Vidal published a landmark paper, “Toward direct brain-computer communication” that both coined the term “brain-computer interface” and set the foundation for an emerging field.

“That whole concept of interacting with and sensing the brain – interpreting signals with a computer and controlling the cursor on a computer with the mind – that paper is pretty much the essence of it,” said Dejan Markovic, a professor of electrical and computer engineering and leader of the Parallel Data Architectures Laboratory. “The real question is: Can we build technologies that enable those types of things that are clinically sustainable, efficacious, and attractive to patients?”

Looking to answer that question, Markovic carries on the legacy of brain-computer interface research at the UCLA Samueli School of Engineering. For nearly a decade, he has been leading the development of a device that would be implanted in the brain to help people with a range of neurological conditions, such as anxiety, depression, or post-traumatic stress disorder.  And he’s been working closely with doctors and scientists at UCLA and UC San Francisco who study the brain.

“The concepts laid out in 1973 by Vidal haven’t changed too much,” he added. “The brain and a computer can ‘talk’ to each other through electrical signals. The big thing that we are trying to change is to be able to quantify what those signals are, and affect functional networks of the brain.”

Markovic’s prototype is a small implantable device with sixty-four electrodes that fan out onto the brain’s surface. With four modules for each electrode, it constitutes a 256-channel system. The system measures tiny electric signals that tell what’s happening in the brain. The device then interprets that data, and responds with electrical pulses, which research has shown can alter mood.

In several ways, it is leaps and bounds more advanced than implants that have come before it. It’s much smaller for one. In fact it’s not immediately noticeable, unless someone’s really looking for it. It has a tiny battery than can be wirelessly charged. The device is also much more sensitive, able to detect and decipher very faint signals from the brain.

Finally, it’s a closed loop system – meaning that while still picking up the brain’s signals, it can modify the frequency and amplitude of the stimulating signal. The system brings much more data into the loop, giving  doctors and scientists more information about what’s happening in real time . Other devices only deliver a constant electric signal, while this new system offers a therapy  that can be more personalized to a particular patient

“Our technology could revolutionize non-pharmacological treatment of brain disorders,” Markovic said. “We want to be able to understand how various indications are expressed in the actual time waveforms, from specific points inside the brain.”

Markovic and UC San Francisco colleagues saw a major breakthrough in an experiment, which was funded by the Defense Advanced Research Projects Agency. A patient with severe anxiety was recorded before and after electrical stimulation was applied. The change in mood following stimulation was immediate and striking.

“For a person to say, ‘now I feel normal, this is me,’ that was the biggest impact point,” he said.

With a series of successful demonstrations, Markovic is now looking to commercialize the technology.  This includes miniaturizing the external device down to just four cubic centimeters. But first, why go with a brain implant in the first place?

“The brain is an electrochemical organ and the vast majority of our treatments for neurological and psychiatric diseases focus on the chemical part,” explained Dr. Nader Pouratian, a UCLA neurosurgeon working with Markovic. “The goal with devices like the one that Dr. Markovic is creating is to target the electrical abnormalities that occur in the brain as a result of neurological and psychiatric disease.”

Added Markovic, “We are looking into patients that have tried pharmaceuticals. In some people, pharmaceuticals have some effect, but there are a sizeable amount of people where pharmaceuticals do not help.”

On a parallel track, Markovic’s technology also offers scientists a powerful magnifying glass into the inner workings of the brain. One of his collaborators is Nanthia Suthana, a UCLA assistant professor at the Jane and Terry Semel Institute for Neuroscience and Human Behavior who studies neuromodulation and neuroimaging.

“The research potential is really endless with such a device,” Suthana said. “Relevant to my own research field, we will be able to investigate the role of single neuron and local field potential activity in freely moving human behaviors such as in spatial navigation, learning and memory.”

“These newer details will allow us to better understand the neuronal mechanisms that support typical human brain functions as well as abnormalities that may occur in neurologic and psychiatric disorders such epilepsy,” she added.


via The brain-computer interface at UCLA – from the 1970s to today | UCLA Samueli School Of Engineering


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[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

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[Abstract + References] Brain Computer Interfaces in Rehabilitation Medicine – PM&R


One innovation currently influencing physical medicine and rehabilitation is brain–computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user’s intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user’s interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.


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[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.


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[Abstract + References] A Scoping Study on the Development of an Interactive Upper-Limb Rehabilitation System Framework for Patients with Stroke – Conference paper


This study aims to propose the framework of the interactive upper-limb rehabilitation system with brain-computer interfaces. The system mainly includes an interactive rehabilitation training platform, a rehabilitation database system, and an EEG and EMG acquisition system. The interactive rehabilitation training system platform includes a virtual rehabilitation game system and an interactive upper-limb rehabilitation device by which a user can perform proactive and reactive rehabilitation.


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[ARTICLE] Comparison of the Efficacy of a Real-Time and Offline Associative Brain-Computer-Interface – Full Text

An associative brain-computer-interface (BCI) that correlates in time a peripherally generated afferent volley with the peak negativity (PN) of the movement related cortical potential (MRCP) induces plastic changes in the human motor cortex. However, in this associative BCI the movement timed to a cue is not detected in real time. Thus, possible changes in reaction time caused by factors such as attention shifts or fatigue will lead to a decreased accuracy, less pairings, and likely reduced plasticity. The aim of the current study was to compare the effectiveness of this associative BCI intervention on plasticity induction when the MRCP PN time is pre-determined from a training data set (BCIoffline), or detected online (BCIonline). Ten healthy participants completed both interventions in randomized order. The average detection accuracy for the BCIonline intervention was 71 ± 3% with 2.8 ± 0.7 min-1 false detections. For the BCIonline intervention the PN did not differ significantly between the training set and the actual intervention (t9 = 0.87, p = 0.41). The peak-to-peak motor evoked potentials (MEPs) were quantified prior to, immediately following, and 30 min after the cessation of each intervention. MEP results revealed a significant main effect of time, F(2,18) = 4.46, p = 0.027. The mean TA MEP amplitudes were significantly larger 30 min after (277 ± 72 μV) the BCI interventions compared to pre-intervention MEPs (233 ± 64 μV) regardless of intervention type and stimulation intensity (p = 0.029). These results provide further strong support for the associative nature of the associative BCI but also suggest that they likely differ to the associative long-term potentiation protocol they were modeled on in the exact sites of plasticity.


Since Daly et al. (2009) proposed the possibility of a Brain-Computer-Interface (BCI) designed for neuromodulation of stroke patients, the field has rapidly expanded with numerous novel BCIs being introduced and tested in the clinic (Ang et al., 2010Broetz et al., 2010Cincotti et al., 2012Li et al., 2013Ramos-Murguialday et al., 2013Mukaino et al., 2014Young et al., 2014Pichiorri et al., 2015Mrachacz-Kersting et al., 2016). To date the main focus has been on upper limb rehabilitation with relatively few targeting lower limb function (for a review see, Teo and Chew, 2014Cervera et al., 2018). In addition, only one group has investigated patients in the sub-acute phases of stroke (Mrachacz-Kersting et al., 2017b), presumably due to the relatively stable condition that a chronic stroke patient presents. Effects from the use of a BCI are thus easier to control since patients in the acute and subacute phase are prone to spontaneous biological recovery (Krakauer and Marshall, 2015).

Typically, BCIs function by collecting the brain signals during a specific state such as performing a movement or motor imagery, extracting features of interest and then translating these into commands for external device control (Daly and Wolpaw, 2008). The available non-invasive BCIs for stroke patients have implemented both electroencephalography (EEG) or near-infrared spectroscopy (NIRS) to acquire the brain signals, extracted various spectral and temporal features [e.g., sensorimotor rhythm, movement related cortical potentials (MR)] and provided diverse types of afferent feedback to the patient such as those generated from using robotic devices, virtual reality or by driving direct nerve or muscular electrical stimulation (for review see, Cervera et al., 2018).

A vital component of any BCI designed for rehabilitation of lost motor function in stroke patients, is that the physiological theories behind learning and memory must be satisfied. One of the most influential theories was proposed in 1949 by Hebb (2005) from which we know that “Cells that fire together, wire together.” Although Hebb proposed his theory on theoretical grounds, animal data later verified that if the pre-synaptic neuron is activated simultaneously with the post-synaptic cell, plasticity is induced, often referred to as long-term potentiation (for a review see, Cooke and Bliss, 2006). In 2000, a group from Rostock University were the first to demonstrate long-term potentiation like plasticity in the intact human brain (Stefan, 2000) with later applications to lower limb muscles (Mrachacz-Kersting et al., 2007). In this intervention [paired associative stimulation (PAS)], a peripheral nerve that innervates the target muscle is activated using a single electrical stimulus and once the generated afferent volley has arrived at the motor cortex, a single non-invasive transcranial magnetic stimulus (TMS) is provided to that area of the motor cortex that has a direct connection to the target muscle (for a review see, Suppa et al., 2017).

In a modified version of PAS, the TMS stimulus has been replaced by the movement related cortical potential (MRCP) (Mrachacz-Kersting et al., 2012). The MRCP, that can be readily measured using EEG, is a slow negative potential that arises approximately 1–2 s prior to movement execution or imagination and attains its peak negativity at the time of movement execution (Walter et al., 1964). This intervention, also termed an associative BCI, induces significant plasticity of the cortical projections to the target muscle and leads to significant functional improvements in chronic and subacute stroke patients (Mrachacz-Kersting et al., 20162017b). In the first phase, patients are asked to attempt 30–50 movements (dorsiflexion of the foot), timed to a visual cue and they receive no sensory feedback. The time of the peak negativity (PN) of the resulting MRCP for every trial is extracted and an average calculated. During the second phase (the actual associative BCI intervention), this time is used to trigger the electrical stimulation of the target nerve such that the generated afferent volley arrives at the motor cortex at precisely peak negativity. Typically, 30–50 such pairings are performed over 3–12 sessions. Since the trigger of the electrical stimulator is not based on the online detection of the MRCP during the second phase, this intervention does not represent a BCI in the classical sense. In the current study the aim was to compare the effects of this associative BCI intervention on plasticity induction as quantified by the motor evoked potential (MEP) following TMS when the MRCP PN time is determined from the phase one trials (BCIoffline modus) or detected during the second phase by using the phase one trials as a training data set (BCIonline modus).[…]


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[Abstract] Mental practice for upper limb rehabilitation after stroke: a systematic review and meta-analysis


Mental practice (MP) is usually provided in combination with other therapies, and new developments for neurofeedback to support MP have been made recently. The objectives of this study were to evaluate the effectiveness of MP and to investigate the intervention characteristics including neurofeedback that may affect treatment outcome. The Cochrane Central Register of Controlled Trials, PubMed, Embase, KoreaMed, Scopus, Web of Science, PEDro, and CIRRIE were searched from inception to March 2017 for randomized controlled trials to assess the effect of MP for upper limb rehabilitation after stroke. Fugl-Meyer Assessment (FMA) was used as the outcome measure for meta-analysis. Twenty-five trials met the inclusion criteria, and 15 trials were eligible for meta-analysis. Among the trials selected for meta-analysis, MP was added to conventional therapy in eight trials or to modified constraint-induced movement therapy in one trial. The other trials provided neurofeedback to support MP: MP-guided neuromuscular electrical stimulation (NMES) in four trials and MP-guided robot-assisted therapy (RAT) in two trials. MP added to conventional therapy resulted in significantly higher FMA gain than conventional therapy alone. MP-guided NMES showed superior result than conventional NMES as well. However, the FMA gain of MP-guided RAT was not significantly higher than RAT alone. We suggest that MP is an effective complementary therapy either given with neurofeedback or not. Neurofeedback applied to MP showed different results depending on the therapy provided. This study has limitations because of heterogeneity and inadequate quality of trials. Further research is requested.


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


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).


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.


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

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[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.




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

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