Posts Tagged BCI

[WEB SITE] FDA Approves MindMotion GO, Mobile Neurorehabilitation Product

The US Food and Drug Administration (FDA) has granted clearance to MindMotion GO, a portable neurorehabilitation product, for launch in the United States.

MindMotion GO utilizes technology that is designed to be used by patients with mild to lightly severe neurological impairments, as well as in the recovery phase of rehabilitation. Produced by the Swiss neurogaming company MindMaze, the mobile rehabilitation product is an outpatient addition to its MindMotion PRO, which received FDA approval in May 2017.

The PRO version differs from the recently approved MindMotion GO in that it is intended for use in patients with severe impairments as well as in early hospital care—in an inpatient setting—with therapeutic activities able to take place within 4 days after a neurological incident.

“Now that both MindMotion products have FDA clearance, MindMaze delivers a full spectrum of neuro-care solutions for both inpatient and outpatient recovery for patients in the United States,” said Tej Tadi, PhD, the CEO and founder of MindMaze, in a statement. “Our unique capability to safely and securely acquire data through our platform is essential for patient recovery and performance, and positions MindMaze as a powerhouse for the future of brain-machine interfaces. Beyond healthcare, this will enable powerful AI-based applications. We are working on a range of brain-tech initiatives at MindMaze to build the infrastructure for innovations to improve patients’ quality of life.”

The mobile MindMotion GO allows for real-time audio and visual feedback, aiding physicians in the assessment of progress and tailoring of therapy to their individual patient’s performance, according to MindMaze. Additionally, it enables the patients to see their progress as well. The set-up and calibration can be done in less than 5 minutes, so patients can begin rehabilitation sessions while physicians facilitate case management.

The program is equipped with a variety of gamified engaging activities which cover motor and task functions and includes a 3D virtual environment. As a result, early findings have suggested that both patient engagement and adherence to therapy have been amplified. Thus far, MindMotion GO has been trialed with upward of 300 patients across therapy centers in the UK, Italy, Germany, and Switzerland.

Neurological impairments are the main cause of long-term disability in the United States, with a recent study estimating direct and indirect costs associated with neurological diseases cost roughly $800 billion annually. For stroke alone, there are almost 800,000 cases each year, with direct annual costs estimated at $22.8 billion.

MindMaze’s Continuum of Care seeks to support earlier, and ongoing, intervention to enable by healthcare providers in the United States to have access to a cost-effective solution for improving neurorehabilitation results.

Even more resources pertaining to stroke prevention and care can be found on MD Magazine‘s new sister site, NeurologyLive.

via FDA Approves MindMotion GO, Mobile Neurorehabilitation Product | MD Magazine

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[WEB SITE] Restoring the function of arms that have been disconnected from the brain

Advances in the control of prosthetic arms, or even exoskeletal arms, continue to amaze. Yet someone with a severe neck injury doesn’t need any such device since the greatest arm they could imagine is sitting right there hanging off their shoulder — but unable to perform. Efforts to control an artificial arm may seem impotent to these folks, when a bridge spanning just a couple centimeters of scar tissue in the spinal column can not even be made. A way forward is now taking shape at Case Western University in Ohio. Researchers there are gearing up to combine the Braingate cortical chip developed at Brown University with their own Functional Electric Stimulation (FES) platform.

It has long been known that electrical stimulation can directly control muscles. The problem is that it is fairly inaccurate, and can be painful or damaging. Stimulating the nerves directly using precisely positioned arrays is a much better approach. One group of Case Western researchers recently demonstrated a remarkable device called a nerve cuff electrode that can be placed around small segments of nerve. They used the cuff to provide an interface for sending data from sensors in the hand back to the brain using sensory nerves in the arm. With FES, the same kind of cuff electrode can also be used to stimulate nerves going the other direction, in other words, to the muscles.

Arm Muscles

The difficulty in such a scheme, is that even if the motor nerves can be physically separated from the sensory nerves and traced to specific muscles, the exact stimulation sequences needed to make a proper movement are hard to find. To achieve this, another group at Case Western has developed a detailed simulation of how different muscles work together to control the arm and hand. Their model consists of 138 muscle elements distributed over 29 muscles, which act on 11 joints. The operational procedure is for the patient to watch the image of the virtual arm while they naturally generate neural commands that the BrainGate chip picks up to move the arm. (In practice, this means trying to make the virtual arm touch a red spot to make it turn green.) Currently in clinical trials, the Braingate2 chip has an array of 96 hair-thin electrodes that is used to stimulate a small region of motor cortex.

The trick here is not just to find any sequence that gets the arm from point A to point B, but to find sequences similar to those that real arms actually use in particular tasks. This is important because each muscle has not only a limited contraction range, but also a limited range where it can actually deliver significant force, and generate feedback signals about those forces. When muscles contract they obviously change shape, but less obvious perhaps, is that their shape at any given moment affects how the other muscles leverage the joints they work. Just as important is the effect of the opposing muscles that control counter movements.

ArmSim

Few movements that we make, even low-force movements, consist of pure contractions of the active muscle and pure inhibition of the opposing muscle. In actuality, muscle units on both sides can be firing in alternating bursts to quickly ratchet joint angles open, particularly when the vector of end-point movement is oblique to the axes of individual arm segments. In other words, even in a simple movement like a bench press, both the biceps and triceps generate forces alternately at various points in the lift, despite the fact that the weight rises uniformly in the upward direction.

If artificial methods of control are going to be used for flesh-and-blood systems, particularly ones that have been idle for some time, overstimulation (or mis-stimulation) when lifting anything even slightly heavy is something to be guarded against. Many sports injuries, such as those in older people performing unfamiliar moves, happen not because they reach too far or too hard, but because their nervous system is not sufficiently practiced to be able to protect the muscle.

While no model for limb movement can be perfect, for the majority of everyday tasks, close may be good enough. The eventual plan is that the patient and the control algorithm will learn together in tandem so that the training screen will not be needed at all. At that point, we might say that Case Western will have a pretty slick interface to offer.

via Restoring the function of arms that have been disconnected from the brain – ExtremeTech

 

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[ARTICLE] Behavioral Outcomes Following Brain–Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial – Full Text

Stroke is a leading cause of persistent upper extremity (UE) motor disability in adults. Brain–computer interface (BCI) intervention has demonstrated potential as a motor rehabilitation strategy for stroke survivors. This sub-analysis of ongoing clinical trial (NCT02098265) examines rehabilitative efficacy of this BCI design and seeks to identify stroke participant characteristics associated with behavioral improvement. Stroke participants (n = 21) with UE impairment were assessed using Action Research Arm Test (ARAT) and measures of function. Nine participants completed three assessments during the experimental BCI intervention period and at 1-month follow-up. Twelve other participants first completed three assessments over a parallel time-matched control period and then crossed over into the BCI intervention condition 1-month later. Participants who realized positive change (≥1 point) in total ARAT performance of the stroke affected UE between the first and third assessments of the intervention period were dichotomized as “responders” (<1 = “non-responders”) and similarly analyzed. Of the 14 participants with room for ARAT improvement, 64% (9/14) showed some positive change at completion and approximately 43% (6/14) of the participants had changes of minimal detectable change (MDC = 3 pts) or minimally clinical important difference (MCID = 5.7 points). Participants with room for improvement in the primary outcome measure made significant mean gains in ARATtotalscore at completion (ΔARATtotal = 2, p = 0.028) and 1-month follow-up (ΔARATtotal = 3.4, p= 0.0010), controlling for severity, gender, chronicity, and concordance. Secondary outcome measures, SISmobility, SISadl, SISstrength, and 9HPTaffected, also showed significant improvement over time during intervention. Participants in intervention through follow-up showed a significantly increased improvement rate in SISstrength compared to controls (p = 0.0117), controlling for severity, chronicity, gender, as well as the individual effects of time and intervention type. Participants who best responded to BCI intervention, as evaluated by ARAT score improvement, showed significantly increased outcome values through completion and follow-up for SISmobility (p = 0.0002, p = 0.002) and SISstrength (p = 0.04995, p = 0.0483). These findings may suggest possible secondary outcome measure patterns indicative of increased improvement resulting from this BCI intervention regimen as well as demonstrating primary efficacy of this BCI design for treatment of UE impairment in stroke survivors.

Introduction

Stroke

Each year there are approximately 800,000 new incidences of stroke in the United States (Benjamin et al., 2017), and in 2010 there were an estimated 16.9 million stroke events globally (Mozaffarian et al., 2015). Stroke occurs as a result of a blockage of blood flow in an area of the brain or by rupture of brain vasculature causing death or damage to local and distal brain tissue. In either etiology, survivors may experience some level of upper extremity (UE) physical impairment. Despite recent advances in acute care, an increasing number of stroke survivors face long-term motor deficits (Benjamin et al., 2017). Costs of care for long-term disability resulting from stroke are substantial with the direct medical costs of stroke estimated to $17.9 billion in 2013 (Benjamin et al., 2017). It is crucial that motor therapy for stroke enhances a survivor’s capacity to autonomously participate in activities of daily living (ADLs), thereby decreasing dependency on caregivers as well as the cost and level of care necessary (Dombovy, 2009Stinear, 2016). Efficacious motor therapy should be designed to improve the overall quality of life for the individual survivor based on their goals and needs (Remsik et al., 2016Stinear, 2016).

Need for Treatment

Survivors in the chronic stage of stroke are the most desperate for rehabilitation. Existing pharmacological treatments and behavioral therapy methods primarily serve to treat symptoms associated with stroke (Benjamin et al., 2017) and may not bring about optimal changes in brain function or connectivity (Power et al., 2011Nair et al., 2015). While a growing population of research suggests the greatest potential for recovery in the post-stroke brain occurs within the first months after insult (Stinear and Byblow, 2014), neuroplastic capacity has been demonstrated in both acute and chronic phases (Caria et al., 2011Ang et al., 2015). Spontaneous biological recovery (SBR) (Beebe and Lang, 2009Cramer and Nudo, 2010) in the initial days and weeks following stoke (acute phase) is thought to represent a critical period in the complex progression of motor recovery, which combines neurobiological processes and learning-related elements. After this window of SBR, it is posited a sensitive period of neurorecovery persists, plateauing around 6 months post-stroke (Wolf et al., 20062010Dromerick et al., 2009Cramer and Nudo, 2010). Traditional rehabilitation therapies generally lose efficacy after such time and the course of standard of care treatment options is exhausted leaving chronically impaired persons with few options.

Potential for Treatment

Motor and cognitive recovery after these initial windows may no longer occur in the same spontaneous nature as is observed during SBR. However, innovative therapeutic techniques show some efficacy generating functional motor recovery beyond the traditional rehabilitation windows (Cramer and Nudo, 2010Ang et al., 2015Irimia et al., 2016). Brain–computer interfaces (BCIs), a novel rehabilitation tool, have shown proof of concept for rehabilitating volitional movements in stroke survivors (Muralidharan et al., 2011Song et al., 20142015Young et al., 2014a,b,c,d2015Irimia et al., 2016). In this growing area of research, developing technologies demonstrate promising potential for treating hemiparesis in a clinically viable and efficient manner and they may offer an avenue to increased autonomy for patients reducing their cost and burden of care.

Effectiveness of Current BCI Therapies

There is currently considerable variability in design and efficacy of BCI therapies as well as little consensus with respect to proper arrangement, administration, and dosing (Muralidharan et al., 2011Ang and Guan, 2013Young et al., 2014aAng et al., 2015Irimia et al., 2016Remsik et al., 2016Bundy et al., 2017Dodd et al., 2017). Although acute stroke care has improved morbidity outcomes significantly, current treatments for persistent UE motor impairment resulting from stroke offer only limited restoration of UE motor function the further from stroke a survivor progresses (Wolf et al., 20062010Dromerick et al., 2009Benjamin et al., 2017Stinear et al., 2017). Evidence suggests both acute and chronic stroke patients respond to various neuro-rehabilitative BCI therapy strategies and can achieve clinically significant changes in measures of UE impairment (Young et al., 2014cIrimia et al., 2016Remsik et al., 2016). Furthermore, recent research also suggests that BCI therapy targeted at motor recovery may provide benefits in other brain regions outside of only the motor network (Mohanty et al., 2018).

Overview of This Study

This post hoc analysis of an ongoing clinical trial (NCT02098265) (Song et al., 20142015Young et al., 2014a,b,c,d2015) evaluates the effects of an interventional, non-invasive closed-loop electroencephalography (EEG)-based BCI intervention for the restoration of distal UE motor function in stroke survivors. Participants who showed measurable change in the primary outcome measure were grouped post hoc. This sub-analysis seeks to identify whether there are participant characteristics strongly associated with motor improvement as measured by primary and secondary outcome measures of UE function. These analyses are intended to inform future BCI research approaches and intervention designs as well as suggest and encourage appropriate participant selection.[…]

 

Continue —>  Frontiers | Behavioral Outcomes Following Brain–Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial | Neuroscience

FIGURE 2. BCI intervention block design: (1) A pre-session open-loop screening task of two attempted and then two imagined grasping tasks (left, right, rest) is used to set control features (BCI classifier) for the forthcoming intervention task (Cursor Task). (2) The closed-loop cursor and target (visual only) intervention condition consists of at least 10 runs of 10 trials of attempted grasping movements for the purpose of guiding a virtual cursor (Ball) either left, or right as cued by the target (Goal) presentation on the horizontal edge of the screen. (3) Following 10 successfully completed runs of the visual only condition, adjuvant stimuli are added to enrich the feedback environment and facilitate volitional movement of the affected extremity (grasping). Subsequent runs are attempted at the preferred pace of the participant, completing as many runs as time allows. (4) With 15 min remaining in the 2-h intervention session, the participant is switched into the post-session open-loop screening task of two imagined and then two attempted grasping tasks (left, right, rest).

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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

Abstract

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

Abstract

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.

References

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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.

Introduction

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).[…]

 

Continue —> Frontiers | Comparison of the Efficacy of a Real-Time and Offline Associative Brain-Computer-Interface | Neuroscience

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[ARTICLE] Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback – Full Text

Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants’ preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.

Introduction

Neurofeedback (NFB) is a novel application of brain-computer interfaces that aids real-time voluntarily regulation of brain activity. Mounting evidence shows that NFB has promising effects to enhance behavior, cognitive and emotional processes in normative samples (1–5). NFB has also been preliminary used to restore aberrant neurobiology and symptoms in neurological conditions (e.g., stroke, traumatic brain injury) and in psychopathology (e.g., ADHD, autism, depression, addiction) (1–7). Real-time functional magnetic resonance imaging (rtfMRI) based NFB has the potential to provide insight in understanding the mechanisms of psychological states (8–10). These include affiliative emotions (11) underpinned by deep brain nuclei (12, 13) the activity of which is unlikely to be robustly measured via surface electroencephalography.

rtfMRI NFB tools can be used to study the causal mechanisms of complex emotions and to inform evidence-based personalized interventions to enhance and recover aberrant emotional states (and their neural substrates) in normative and clinical samples. One key practical human challenge of fMRI studies includes participants being distracted and experiencing difficulties to feel valid psychological states in the scanner environment, particularly when trying to sustain complex emotions.

Recent studies have combined immersive virtual environments with multiple sensory modalities to deliver psychological/cognitive interventions, and to enhance their effectiveness via engaging and motivating individuals to practice (14–16).

Only two proof of concept studies have combined rt-NFB with virtual environments as brain computer interfaces (BCI). An electroencephalography-based NFB study computed brain activity from about 500 participants collectively, during an interactive game of relaxation and concentration over one night (16), where individual’s level of brain activity could not be discerned. A separate rtfMRI-NFB paradigm used a virtual fire interface to up-regulate and down-regulate brain activity in eight healthy participants—but this was devoid of any emotional states and far from multimodal and immersive (17).

It remains untested whether rt-NFB platforms integrating multisensory virtual environments can successfully recruit complex emotions and sustain these emotions long and strong enough to probe their underlying neural correlates. Such a platform can advance NFB applications, via (i) increasing the ecological validity of rtfMRI-NFB experiments, and their relevance for the daily experiences of emotions outside of experimental settings, (ii) adapting NFB interfaces to the individual and target population so these are more relatable, engaging and effective in generating and sustaining complex emotions that maximize the success of rtfMRI-NFB interventions (18–20).

This study aims to demonstrate the feasibility of an engaging rtfMRI-NFB interface that can be individually tailored and, specifically, to provide a proof of concept for a rtfMRI-NFB integrating a virtual environment as a BCI and musical stimuli using both local (region of interest, ROI) and distributed (support vector machine, SVM) analyses. The FRIEND Engine Framework system (21) was enhanced and adapted for this aim. We recruited healthy young adults performing rtfMRI-NFB during complex emotion experiences, including tenderness—a positive affiliative emotion – and anguish—a self-reflective negative emotion (11, 13, 22–25).

We also aimed to validate the functional anatomy of these complex emotions during rtfMRI-NFB. After the real-time data was collected, we ran offline fMRI data analyses to verify the effects of the real-time neurofeedback task on brain activity using standard preprocessing and statistical analysis methods.

We hypothesized that participants would voluntary change the color of a virtual environment in the BCI during rtfMRI-NFB using the activity of the following regions: (i) for the tenderness condition, the septo-hypothalamic area (when using ROI-based rtfMRI-NFB method) and other brain areas ascribed to positive affiliative emotions i.e., medial orbitofrontal areas (when using SVM-based rtfMRI-NFB method) (11, 25–27); and (ii) for the anguish condition, the amygdala (during the ROI-based fMRI-NFB method) and also lateral prefrontal cortices implicated in negative affect (e.g., anguish, fear, anxiety, negative mood, stress, psychological pain), and in psychopathologies where negative affect is a feature [e.g., depression and generalized anxiety disorder (28–32)] (during SVM-based rtfMRI-NFB).

Materials and Methods

Participants

We used a single subject, repeated measures design with two identical assessments on two consecutive days, counterbalanced by rtfMRI-NFB method (i.e., ROI and SVM). We recruited eight psychiatrically and neurologically healthy postgraduate research students, free of psychoactive medication and with normal or corrected-to-normal vision. Four participants were recruited from the D’Or Institute for Research and Education (IDOR) in Rio de Janeiro, Brazil (approved by the Ethics and Scientific committees of the Copa D’Or Hospital, Rio de Janeiro, Brazil – No 922.218). To validate the protocol in a different scanner and institution, we also recruited four participants from the Monash Biomedical Imaging (MBI) at Monash University in Melbourne, Australia (MUHREC CF15/1756 – 2015000893). All volunteers provided written informed consent prior to study participation.

Design of the Neurofeedback BCI

Supplementary video 1 and Figure 1 show the BCI used for the rt-fMRI NFB. The BCI comprised a virtual environment as a medium to convey sensory feedback to participants in real time, in association with ongoing tenderness, anguish and neutral emotional states. The virtual environment was created by editing the Unity 3D asset Autumnal Nature Pack (Unity 3D, https://assetstore.unity.com/packages/3d/environments/autumnal-nature-pack-3649) and displayed a first-person navigation at walking speed through hills and cornfields, with a total duration of 10′8″ (Supplementary Video 1). The virtual environment was prepared to alternate between different trial types: neutral (30″), tenderness (46″) and anguish (46″).

The trial types were displayed via changes in the base color hues of the virtual environment and via specific music excerpts. Music excerpts were fixed for each trial type, and not influenced by current neural/psychological states (no music for Neutral, mild, gentle music for Tenderness and eerie, distorted music for Anguish). Music excerpts were selected from 20 audio tracks, all normalized using the root mean square feature of Audacity software (Audacity, http://www.audacityteam.org). The audio tracks were previously rated to have comparable volume, pace, and rhythm. For the rtfMRI-NFB task runs, four excerpts for tenderness and four excerpts for anguish were played.

Neutral trials were characterized by a normal colored virtual landscape displayed in the BCI with no background music. Tenderness trials were characterized by a change in the color of the virtual landscape to orange and were accompanied by tenderness music excerpts. Anguish trials commenced when the color of the environment turned to purple hues and were accompanied by anguish music excerpts.

Neurofeedback Task

Task Practice Outside the MRI

For training purposes, we recorded a video showing a sample of the virtual environment. The video lasted as long as one run of the rtfMRI-NFB task (10′ 8″) and was used by participants to practice tenderness, anguish and neutral states before the MRI. With this practice, participants could learn which music tracks and VR color changes in the BCI corresponded to tenderness, anguish and neutral trials.

Neurofeedback Interface

As shown in Figure 1, instead of a classic thermometer, the color of the virtual environment was used as BCI changed in real time with increased engagement of the neural activity/pattern corresponding to distinct target emotional states—orange for tenderness trials, purple for anguish trials and natural light tones for neutral trials. Participants were instructed to experience tenderness or anguish as intensely as possible in the respective trials and to increase the intensity of their emotion to turn in real time, the color of the virtual environment BCI to as orange as possible during tenderness trials, and as purple as possible during anguish trials, which increased in turn the corresponding neural activity/pattern.

FIGURE 1

Figure 1. Color hue modulation of the virtual environment during rtfMRI-NFB. The color hue changes from baseline neutral trials to a more intense orange and purple as participants increasingly engage target brain regions for tenderness and anguish trials.

[…]

via Frontiers | Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback | Neurology

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

Introduction/Background

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

Material and method

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

Results

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

Conclusion

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

 

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

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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.

 

stroke-brain-computer-interface

 

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

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

Combined therapy tested on stroke patients

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

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

 

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

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

 

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

The study results were published in Nature Communications.

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