Posts Tagged Brain Computer Interface

[Abstract] Motor Imagery Based Brain-Computer Interface Control of Continuous Passive Motion for Wrist Extension Recovery in Chronic Stroke Patients

Highlights

  • Twenty-one patients successfully recovered active wrist extension.
  • Motor imagery based BCI control of wrist CPM training was applied.
  • Typical spatial and spectrum patterns of ERD/ERS formed after training.

Abstract

Motor recovery of wrist and fingers is still a great challenge for chronic stroke survivors. The present study aimed to verify the efficiency of motor imagery based brain-computer interface (BCI) control of continuous passive motion (CPM) in the recovery of wrist extension due to stroke. An observational study was conducted in 26 chronic stroke patients, aged 49.0 ± 15.4 years, with upper extremity motor impairment. All patients showed no wrist extension recovery. A 24-channel highresolution electroencephalogram (EEG) system was used to acquire cortical signal while they were imagining extension of the affected wrist. Then, 20 sessions of BCI-driven CPM training were carried out for 6 weeks. Primary outcome was the increase of active range of motion (ROM) of the affected wrist from the baseline to final evaluation. Improvement of modified Barthel Index, EEG classification and motor imagery pattern of wrist extension were recorded as secondary outcomes. Twenty-one patients finally passed the EEG screening and completed all the BCI-driven CPM trainings. From baseline to the final evaluation, the increase of active ROM of the affected wrists was (24.05 ± 14.46)˚. The increase of modified Barthel Index was 3.10 ± 4.02 points. But no statistical difference was detected between the baseline and final evaluations (P > 0.05). Both EEG classification and motor imagery pattern improved. The present study demonstrated beneficial outcomes of MI-based BCI control of CPM training in motor recovery of wrist extension using motor imagery signal of brain in chronic stroke patients.

 

Graphical abstract

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[ARTICLE] The Integration of Brain-Computer Interface (BCI) as Control Module for Functional Electrical Stimulation (FES) Intervention in Post-Stroke Upper Extremity Rehabilitation – Full Text

ABSTRACT

One of the prevalent disabilities after stroke is the loss of upper extremity motor function, leading survivors to suffer from an increased dependency in their activities of daily living and a general decrease in their overall quality of life. Therefore, the restoration of upper extremity function to improve survivors’ independency is crucial. Conventional stroke rehabilitation interventions, while effective, fall short of helping individuals achieve maximum recovery potential. Functional Electrical Stimulation (FES), both with passive and active approaches, has been found to moderately increase function in the affected limbs. This paper discusses a novel EEG-Based BCI-FES system that provides FES stimulation to the affected limbs based on the brain activity patterns of the patient specifically in the sensory motor cortex, while the patient imagines moving the affected limb. This system allows the synchronization of brain activity with peripheral movements, which may lead to brain reorganization and restoration of motor function by affecting motor learning or re-learning, and therefore induce brain plasticity to restore normal-like brain function.

INTRODUCTION

Stroke is one of the leading causes of severe motor disability, with approximately 800,000 individuals each year are experiencing a new or recurrent stroke in the US alone (1). Advances in healthcare and medical technology, and the high incidence of stroke and its increasing rate in the growing elderly population, have contributed to a relatively large population of stroke survivors currently estimated at 4 million individuals in the United States alone (1). These survivors are left with several devastating long-term neurological impairments.

The most apparent defect after a stroke is motor impairments, with impairment of upper extremity (UE) functions standing as the most disabling motor deficit. Approximately 80% of survivors suffering from UE paresis, and only about one-tenth of the them regain complete functional recovery (2). Stroke survivors generally suffer from a decrease in their quality of life, and an increase dependency in their activities of daily living. Statistically, close to one quarter of the stroke survivors become dependent in activities of daily living (3). Thus, the optimal restoration of arm and hand function is crucial to improve their independence.

Recently, several remarkable advancements in the medical management of stroke have been made. However, a widely applicable or effective medical treatment is still missing, and most post-stroke care will continue to depend on rehabilitation interventions (4). The available UE stroke rehabilitation interventions can be categorized as: conventional physical and occupational therapy, constraint-induced movement therapy (CIT), functional electrical stimulation (FES), and robotic-aided and sensor-based therapy systems (5). Although an increased effort has been made to enhance the recovery process following a stroke, survivors generally do not reach their full recovery potential. Thus, the growing population of stroke survivors is in a vital need for innovative strategies in stroke rehabilitation, especially in the domain of UE motor rehabilitation. This paper presents an innovative integration of a brain-computer interface (BCI) system to actively control the delivery of FES. Early research and product development activities are advancing the reality of this becoming a mainstream intervention option.

PASSIVE VS. ACTIVE DELIVERY OF FES

The use of FES on the impaired arm is an accepted intervention for stroke rehabilitation aiming to improve motor function. A systematic review with meta-analysis of 18 randomized control trials found that FES had a moderate effect on activity compared with no intervention or placebo and a large effect on UE activity compared to control groups, suggesting that FES should be used in stroke rehabilitation to improve the ability to perform activities (6). Specifically, improvements in UE motor function after intensive FES intervention can be ascribed to the increased ability to voluntarily contract impaired muscles, the reduction in spasticity and improved muscle tone in the stimulated muscles, and the increased range of motion in all joints (7). These improvements in UE after FES could be attributable to multiple neural mechanisms, with one mechanism suggesting that proprioceptive sensory input and visual perception of the movement could promote neural reorganization and motor learning (8). A potential limiting factor to the application of FES is that the stimulation is administered manually, usually from a therapist, without any regard to the concurrent brain activity of the patient. This makes the delivery a passive process with no to minimal coordination with the mental task required to happen concurrently from the patient.

On the other hand, electromyography (EMG)-triggered FES systems made the delivery of FES an active process. Such systems are activated through detecting a preset electrical threshold in certain muscles, which provide the user (patient) the ability to actively control the delivery of FES and make the delivery concurrent with the patient’s brain activity. However, a systematic review of 8 randomized controlled trials (n=157) that assessed the effects of EMG-triggered neuromuscular electrical stimulation for improving hand function in stroke patients found no statistically significant differences in effects when compared to patients receiving usual care (9). A possibility to explain the shortcoming of EMG-triggered FES systems, is that the ability of the brain to generate and send efficient neural signals to the peripheral nervous system is disrupted after stroke, which could affect the control mechanism of these systems. Thus, the synchronization of FES with brain activity maybe critical for the optimization of recovery.

AN ACTIVE EEG-BASED BCI-FES SYSTEM

BCI technology can be used to actively control the FES application through detecting the brain neural activity directly when imagining or attempting a movement. Performing or mentally imagining (or as it commonly called motor imagery) a movement results in the generation of neurophysiological phenomena called event-related desynchronization or synchronization (ERD or ERS). ERD or ERS can be observed from Mu (9–13 Hz) or Beta rhythms (22–29 Hz) over the primary sensorimotor area contralateral to the imagined part of the body (10). These rhythms can be detected using electroencephalography (EEG). Therefore, an EEG based BCI system can be utilized to provide automated FES neurofeedback through detecting either actual movement or motor imagery (MI) and can be used to train the voluntary modulation of these rhythms. The ability to modulate these rhythms alongside the real-time neurofeedback from the FES application may induce neuroplastic change in a disrupted motor system to allow for more normal motor-related brain activity, and thus promote functional recovery. Figure 1 provides an overview of the BCI-FES system.

Any BCI-FES intervention session includes two screening tasks: an open-loop screening followed by a closed-loop task. The open-loop screening task is used to identify appropriate EEG-based control features to guide all subsequent closed-loop tasks. In the open-loop screening task, subjects are instructed to perform attempted movement of either hand by following on-screen cues of “right”, “left”, and “rest”. The attempted movement can vary across subjects, depending on the subject’s baseline abilities and recovery goals. For example, subjects can perform opening and closing of the hand or wrist flexion/extension movements. During this screening task, no feedback is provided to the subject.

figure 2 shows a screenshot of the closed-loop task interface, with a ball at the center and a target to the right, in order to provide a cue for the user to move his/her right hand.

Figure 2. Screenshot of Closed-loop Task

Data from the open-loop screening task will then be analyzed to identify appropriate EEG-based control features by determine the EEG channels the presents the largest r-squared values within the frequency ranges of the Mu and Beta rhythms for each attempted movement using left or right hand (11). The identified channels and the specific frequency bins will then be used to control the signals for the closed-loop neurofeedback task.

In the closed-loop screening task, a real-time visual feedback is given to the subject in a form of a game. A ball appears on the center of a computer monitor with a vertical rectangle (target) to either the right or left side of the screen (Figure 2). The movement of the ball is controlled by the BCI system in which the detection of an attempted movement in either hand will be translated into moving the ball toward the same side. For example, if the target appeared on the left side of the screen and the BCI system detected a movement attempt of the user’s left hand, the ball then moves toward the left. Users are instructed to perform or attempt the same movement that they used during the open-loop task. The FES electrodes are placed on the subject’s affected side over a specific muscle of the forearm. The selection of which muscle to be innervated with FES is dependent on the rehabilitation goal for the subject. For example, if a subject is having a difficulty extending his/her wrist, the FES electrodes are placed over the extensor muscles of the impaired forearm.

The FES neurofeedback is triggered when cortical activity related to attempted movement of the impaired limb is detected by the BCI system, and the subject is cued to attempt movement of the impaired hand. Thus, since both ball movement and FES are controlled by the same set of EEG signals, FES is only applied when the ball moves correctly toward the target on the affected side of the body. This triggering of the FES ensures that only consistent, desired patterns of brain activity associated with attempted movement of the impaired hand are rewarded with feedback from the FES device.

DISCUSSION

The growing population of stroke survivors constitutes an increasing need for new strategies in stroke rehabilitation. Thus, it is imperative to explore novel intervention technologies that present promise to aid in the recovery process of this population. Some studies suggest that noninvasive EEG-based BCI systems hold a potential for facilitating upper extremities motor recovery after stroke (12,13). Although several groups had gave up on the idea of using non-invasive EEG-based BCI systems for control, there might be several implementations of these systems in the context of rehabilitation that yet need to be explored. The active EEG-based BCI-FES system is one example. However, more research and clinical studies are needed to investigate the efficacy of the system in order to be accepted and integrated into regular stroke rehabilitation practice.

REFERENCES

(1) Norrving B, Kissela B. The global burden of stroke and need for a continuum of care. Neurology 2013 Jan 15;80(3 Suppl 2):S5-12.

(2) Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. The Lancet Neurology 2009;8(8):741-754.

(3) Sanchez RJ, Liu J, Rao S, Shah P, Smith R, Rahman T, et al. Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravity-reduced environment. IEEE Transactions on neural systems and rehabilitation engineering 2006;14(3):378-389.

(4) Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. The Lancet 2011;377(9778):1693-1702.

(5) Loureiro RC, Harwin WS, Nagai K, Johnson M. Advances in upper limb stroke rehabilitation: a technology push. Med Biol Eng Comput 2011;49(10):1103.

(6) Howlett OA, Lannin NA, Ada L, McKinstry C. Functional electrical stimulation improves activity after stroke: a systematic review with meta-analysis. Arch Phys Med Rehabil 2015;96(5):934-943.

(7) Kawashima N, Popovic MR, Zivanovic V. Effect of intensive functional electrical stimulation therapy on upper-limb motor recovery after stroke: case study of a patient with chronic stroke. Physiotherapy Canada 2013;65(1):20-28.

(8) Wang R. Neuromodulation of effects of upper limb motor function and shoulder range of motion by functional electric stimulation (FES). Operative Neuromodulation: Springer; 2007. p. 381-385.

(9) Meilink A, Hemmen B, Seelen H, Kwakkel G. Impact of EMG-triggered neuromuscular stimulation of the wrist and finger extensors of the paretic hand after stroke: a systematic review of the literature. Clin Rehabil 2008;22(4):291-305.

(10) Ang KK, Guan C. EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2017;25(4):392-401.

(11) Wilson JA, Schalk G, Walton LM, Williams JC. Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000. J Vis Exp 2009 Jul 29;(29). pii: 1319. doi(29):10.3791/1319.

(12) Caria A, Weber C, Brötz D, Ramos A, Ticini LF, Gharabaghi A, et al. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology 2011;48(4):578-582.

(13) Young BM, Nigogosyan Z, Remsik A, Walton LM, Song J, Nair VA, et al. Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. Frontiers in neuroengineering 2014;7:25.

ACKNOWLEDGMENT

This project is supported in part by UW-Madison Institute for Clinical and Translational Research, and College of Health Sciences, UW-Milwaukee.

 

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[Abstract + References] A Wireless BCI-FES Based on Motor Intent for Lower Limb Rehabilitation

Abstract

Recent investigations have proposed brain computer interfaces combined with functional electrical stimulation as a novel approach for upper limb motor recovery. These systems could detect motor intention movement as a power decrease of the sensorimotor rhythms in the electroencephalography signal, even in people with damaged brain cortex. However, these systems use a large number of electrodes and wired communication to be employed for gait rehabilitation. In this paper, the design and development of a wireless brain computer interface combined with functional electrical stimulation aimed at lower limb motor recovery is presented. The design requirements also account the dynamic of a rehabilitation therapy by allowing the therapist to adapt the system during the session. A preliminary evaluation of the system in a subject with right lower limb motor impairment due to multiple sclerosis was conducted and as a performance metric, the true positive rate was computed. The developed system evidenced a robust wireless communication and was able to detect lower limb motor intention. The mean of the performance metric was 75%. The results encouraged the possibility of testing the developed system in a gait rehabilitation clinical study.

References

  1. 1.
    Pfurtscheller, G., Mcfarland, D.: BCIs that use sensorimotor rhythms. In: Wolpaw, J.R., Wolpaw, E. (eds.) Brain-Computer Interfaces: Principles and Practice, pp. 227–240. Oxford University Press (2012)Google Scholar
  2. 2.
    Carrere, L.C., Tabernig, C.B.: Detection of foot motor imagery using the coefficient of determination for neurorehabilitation based on BCI technology. IFMBE Proc. 49, 944–947 (2015).  https://doi.org/10.1007/978-3-319-13117-7_239CrossRefGoogle Scholar
  3. 3.
    Sannelli, C., Vidaurre, C., Müller, K.R., Blankertz, B.: A large scale screening study with a SMR-based BCI: categorization of BCI users and differences in their SMR activity (2019)Google Scholar
  4. 4.
    Do, A.H., Wang, P.T., King, C.E., Schombs, A., Cramer, S.C., Nenadic, Z.: Brain-computer interface controlled functional electrical stimulation device for foot drop due to stroke, pp. 6414–6417 (2012)Google Scholar
  5. 5.
    Ramos-Murguialday, A., Broetz, D., Rea, M., Yilmaz, Ö., Brasil, F.L., Liberati, G., Marco, R., Garcia-cossio, E., Vyziotis, A., Cho, W., Cohen, L.G., Birbaumer, N.: Brain-Machine-interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100–108 (2014).  https://doi.org/10.1002/ana.23879.Brain-Machine-InterfaceCrossRefGoogle Scholar
  6. 6.
    Biasiucci, A., Leeb, R., Iturrate, I., Perdikis, S., Al-Khodairy, A., Corbet, T., Schnider, A., Schmidlin, T., Zhang, H., Bassolino, M., Viceic, D., Vuadens, P., Guggisberg, A.G., Millán, J.D.R.: Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat. Commun. 9, 1–13 (2018).  https://doi.org/10.1038/s41467-018-04673-zCrossRefGoogle Scholar
  7. 7.
    Tabernig, C.B., Lopez, C.A., Carrere, L.C., Spaich, E.G., Ballario, C.H.: Neurorehabilitation therapy of patients with severe stroke based on functional electrical stimulation commanded by a brain computer interface. J. Rehabil. Assist. Technol. Eng. 5, 205566831878928 (2018).  https://doi.org/10.1177/2055668318789280CrossRefGoogle Scholar
  8. 8.
    McCrimmon, C.M., King, C.E., Wang, P.T., Cramer, S.C., Nenadic, Z., Do, A.H.: Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J. Neuroeng. Rehabil. 12 (2015).  https://doi.org/10.1186/s12984-015-0050-4
  9. 9.
    g.Nautilus wireless biosignal acquisition Homepage. http://www.gtec.at/Products/Hardware-and-Accessories/g.Nautilus-Specs-Features
  10. 10.
    Emotiv EpocFlex flexible wireless EEG system Homepage. https://www.emotiv.com/epoc-flex/
  11. 11.
    Vuckovic, A., Wallace, L., Allan, D.: Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. J. Neurol. Phys. Ther. 39, 3–14 (2015)CrossRefGoogle Scholar
  12. 12.
    Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004).  https://doi.org/10.1109/TBME.2004.827072CrossRefGoogle Scholar
  13. 13.
    McCrimmon, C.M., Fu, J.L., Wang, M., Lopes, L.S., Wang, P.T., Karimi-Bidhendi, A., Liu, C.Y., Heydari, P., Nenadic, Z., Do, A.H.: Performance assessment of a custom, portable, and low-cost brain-computer interface platform. IEEE Trans. Biomed. Eng. 64, 2313–2320 (2017).  https://doi.org/10.1109/TBME.2017.2667579CrossRefGoogle Scholar

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[ARTICLE] Brain–computer interface and assist-as-needed model for upper limb robotic arm – Full Text

Post-stroke paralysis, whereby subjects loose voluntary control over muscle actuation, is one of the main causes of disability. Repetitive physical therapy can reinstate lost motions and strengths through neuroplasticity. However, manually delivered therapies are becoming ineffective due to scarcity of therapists, subjectivity in the treatment, and lack of patient motivation. Robot-assisted physical therapy is being researched these days to impart an evidence-based systematic treatment. Recently, intelligent controllers and brain–computer interface are proposed for rehabilitation robots to encourage patient participation which is the key to quick recovery. In the present work, a brain–computer interface and assist-as-needed training paradigm have been proposed for an upper limb rehabilitation robot. The brain–computer interface system is implemented with the use of electroencephalography sensor; moreover, backdrivability in the actuator has been achieved with the use of assist-as-needed control approach, which allows subjects to move the robot actively using their limited motions and strengths. The robot only assists for the remaining course of trajectory which subjects are unable to perform themselves. The robot intervention point is obtained from the patient’s intent which is captured through brain–computer interface. Problems encountered during the practical implementation of brain–computer interface and achievement of backdrivability in the actuator have been discussed and resolved.

The recovery of upper limb motions and strengths in patients with damaged neuromuscular system via robotic rehabilitation devices is a promising way of enhancing existing treatments and their efficacies. Various reasons may cause limb dysfunctions, including stroke, spinal cord injuries, or even ligament rupture. According to the World Health Organization, about 15 million people globally suffer from Cerebro-Vascular Accidents (CVAs) each year and up to 65% of these need limb recovery procedures.1 Only in the last 15 years, the number of CVA or stroke patients is increased by 40%, which is the result of a more intense pace of living, deterioration of ecology, and increased aging population.2 Considering these statistics, development of new and efficient ways of rehabilitation is just as important as implementation of improved prevention strategies.

For the last 20 years, robotics-based therapy was steadily paving its way for becoming an essential practice in rehabilitation medicine.3,4 According to the systematic review of Kwakkel et al.5 on the upper limb recovery using robot-aided therapy, repetitive, meaningful, labor-intensive treatment programs implemented with robotic devices provide positive impact for the restoration of functional abilities in human limbs. In medical terminology, a device that provides support, and aligns or improves the function of movable limbs is known as orthosis, and robotic devices intended to provide such treatment are called robotic orthoses.6 Particularly, two key directions gained major attention in the medical engineering research: robot-assisted therapy and functional electrical simulation (FES) therapy. The FES therapy describes a technique that stimulates weakened or paralyzed muscles on a human limb by applying electric charges externally. The goal of FES therapy is to reactivate the neural connections between a muscle and human’s sensorimotor system to enable patients’ ability to control their limbs without assistance.7 In the study by Popovic and others, the functional electrical therapy (FET) was applied with the use of surface electrodes and it was used to stimulate arm fingers of patients, this therapy has demonstrated positive therapeutic effects.8 It was revealed that daily 30-min therapy for 1-month period allowed improvement in movement range, speed, and increased strength in muscles. There are also side effects of FES-based treatment such as pain and irritation on the affected area, autonomic dysreflexia, increased spasticity, broken bones, and mild electric shocks from faulty equipment. However, the robot-assisted rehabilitation is non-invasive and free from above risks, and it is preferred for the rehabilitation of stroke survivors.

The important advantage of robotic devices is that they can reduce the burden on health care workers who traditionally had to conduct labor-intensive training sessions for patients. Equipped with sensors, intelligent controllers, and haptic and visual interfaces, robotic orthosis can have a potential to put the recovery process to a new level by collecting relevant data about various health parameters (pulse rate, body temperature, etc.) and adjusting the training modes accordingly. Besides the positive impacts of robot-based rehabilitation, the reliability of robot-based assistance is still questionable and adversely it may worsen the recovery progress made before, and that depends on the type of assistance control robot employs.9 Assist-as-needed (AAN) control type has become one of the prominent strategies recently which has been recommended positively from clinical trials.10 In order to stabilize the system, AAN-based approach has become subject to be researched by scientists. In the work done by Wolbrecht, AAN control is obtained from the adaptive control by incorporating novel force to address and decrease the system’s parametric errors.11 There are also other works which propose AAN type of control for their systems;1214 however, there are no works which have incorporated both BCI (brain–computer interface)- and AAN-based control approach into the system.

Owing to the recent advances in biosensors, especially in their robustness and signal processing, robot controllers equipped with bio-sensing are able to achieve intelligence with less complex algorithms. One of the most recent applications of BCI is in the domain of orthoses.1517 Newer instances of orthoses combine latest advances in control theory and brain activity. Berlin Technical University in cooperation with Korean University created an exoskeleton to maneuver lower limbs. A feature of this work is the use of non-invasive electroencephalography (EEG). The study involved 11 healthy men aged 25 to 32 years.18 First upper limb exoskeleton controlled by BCI was proposed by AA Frolov et al.19 Authors concluded that BCI inclusion improves the movements of the paretic hand in post-stroke patients irrespective of severity and localization of the disease. In addition, it was shown that duration of the training also increases effectiveness of rehabilitation.

Based on the letters on the screen, it was possible to determine native language of the patient in the work done by Vasileva.20 In this work, non-invasive EEG had been used. However, it was noted that non-invasive devices have less accuracy than professional medical EEG equipment. To improve signal detection, Agapov et al.21 have developed advanced algorithm of processing visually evoked potentials. To visualize stimuli, “eSpeller” software was developed.

Motivated by the above-mentioned successes and advances, in the present work, possible use of BCI is investigated in the rehabilitation robots for the treatment of stroke survivors. The aim of this work is to develop EEG-based mechatronic system that can receive electrical brain signals, detect emotions and gestures of the patient, and intelligently control robotic arm. In addition, to ensure smooth and compliant movement of the rehabilitation robot and improve treatment efficacy, AAN control paradigm is also considered. This research used EEG package and a controller to develop BCI system and realize AAN-based control. Developed system can help patients to control robot with their thoughts and enhance their participation in the rehabilitation process. Methodology of the current work is explained in the “Methodology” section, and in the subsequent sections, results are discussed before drawing conclusions from this research work.

EEG sensor

In order to register the brain activity, 16 EEG electrodes distributed around the patient’s head have been used. To provide more information which is related to motor imaginary signals, the frequency characteristics were extracted from the data by converting them from the time domain to the frequency domain. Furthermore, to distinguish between movement intentions and rest positions, bandpass filter in the range of 5 to 40 Hz was used.22,23 Since EEG data set recording can be very large, the powerful surface Laplacian technique was applied to lower the risk of influence from the neighboring neurons on the crucial cerebral cortex neurons.24 Finally, only dominant frequency of 13 to 30 Hz, also known as beta wave frequency, was featured according to Gropper et al.25 This band distinction was benchmarker as a sensible area of resting brain activity.

Abiding by the previous works associated with EEG signal processing in Iáñez et al.26 and Hortal et al.,27 the feature selection was reduced to the group of 29 features, which later were used for the further classification and predictive model construction.

After receiving data using an EEG, algorithm needs to determine the desired effect for the user. Input data for this algorithm are EEG signals recorded during the demonstration of stimuli. In most of the currently existing studies on this subject, the problem of classifying signals is divided into three large subtasks:

  • Preprocessing the signal (in order to remove noise components);
  • Formation of a feature space;
  • Classification of objects in the constructed feature space.

It should be noted that the greatest influence on the final quality of the classification is made by the extent to which the task of forming the feature space was successfully accomplished. The general scheme of operation of BCI is depicted in Figure 1.


                        figure

Figure 1. Block diagram of BCI interface.

 

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Continue —>  Brain–computer interface and assist-as-needed model for upper limb robotic arm – Akim Kapsalyamov, Shahid Hussain, Askhat Sharipov, Prashant Jamwal, 2019

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Figure 4. (a) ELA actuated upper limb rehabilitation robot, (b) upper limb rehabilitation robot in use, and (c) robotic orthosis in use with EEG sensor.

 

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[ARTICLE] Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report – Full Text

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.

Introduction

Worldwide, stroke is a leading cause of adult long-term disability (Mozaffarian et al., 2015). From those who survive, an increased number is suffering with severe cognitive and motor impairments, resulting in loss of independence in their daily life such as self-care tasks and participation in social activities (Miller et al., 2010). Rehabilitation following stroke is a multidisciplinary approach to disability which focuses on recovery of independence. There is increasing evidence that chronic stoke patients maintain brain plasticity, meaning that there is still potential for additional recovery (Page et al., 2004). Traditional motor rehabilitation is applied through physical therapy and/or occupational therapy. Current approaches of motor rehabilitation include functional training, strengthening exercises, and range of movement exercises. In addition, techniques based on postural control, stages of motor learning, and movement patterns have been proposed such as in the Bobath concept and Bunnstrom approach (amongst others) (Bobath, 1990). After patients complete subacute rehabilitation programs, many still show significant upper limb motor impairment. This has important functional implications that ultimately reduce their quality of life. Therefore, alternative methods to maximize brain plasticity after stroke need to be developed.

So far, there is growing evidence that action observation (AO) (Celnik et al., 2008) and motor imagery (MI) improve motor function (Mizuguchi and Kanosue, 2017) but techniques based on this paradigm are not widespread in clinical settings. As motor recovery is a learning process, the potential of MI as a training paradigm relies on the availability of an efficient feedback system. To date, a number of studies have demonstrated the positive impact of virtual-reality (VR) based on neuroscientific grounds on recovery, with proven effectiveness in the stroke population (Bermúdez i Badia et al., 2016). However, patients with no active movement cannot benefit from current VR tools due to low range of motion, pain, fatigue, etc. (Trompetto et al., 2014). Consequently, the idea of directly training the central nervous system was promoted by establishing an alternative pathway between the user’s brain and a computer system.

This is possible by using electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), since they can provide an alternative non-muscular channel for communication and control to the external world (Wolpaw et al., 2002), while they could also provide a cost-effective solution for training (Vourvopoulos and Bermúdez, 2016b). In rehabilitation, BCIs could offer a unique tool for rehabilitation since they can stimulate neural networks through the activation of mirror neurons (Rizzolatti and Craighero, 2004) by means of action-observation (Kim et al., 2016), motor-intent and motor-imagery (Neuper et al., 2009), that could potentially lead to post-stroke motor recovery. Thus, BCIs could provide a backdoor to the activation of motor neural circuits that are not stimulated through traditional rehabilitation techniques.

In EEG-based BCI systems for motor rehabilitation, Alpha (8–12 Hz) and Beta (12–30 Hz) EEG rhythms are utilized since they are related to motor planning and execution (McFarland et al., 2000). During a motor attempt or motor imagery, the temporal pattern of the Alpha rhythms desynchronizes. This rhythm is also named Rolandic Mu-rhythm or the sensorimotor rhythm (SMR) because of its localization over the sensorimotor cortices. Mu-rhythms are considered indirect indications of functioning of the mirror neuron system and general sensorimotor activity (Kropotov, 2016). These are often detected together with Beta rhythm changes in the form of an event-related desynchronization (ERD) when a motor action is executed (Pfurtscheller and Lopes da Silva, 1999). These EEG patterns are primarily detected during task-based EEG (e.g., when the participant is actively moving or imagining movement) and they are of high importance in MI-BCIs for motor rehabilitation.

A meta-analysis of nine studies (combined N = 235, sample size variation 14 to 47) evaluated the clinical effectiveness of BCI-based rehabilitation of patients with post-stroke hemiparesis/hemiplegia and concluded that BCI technology could be effective compared to conventional treatment (Cervera et al., 2018). This included ischemic and hemorrhagic stroke in both subacute and chronic stages of stoke, between 2 to 8 weeks. Moreover, there is evidence that BCI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis (Ramos-Murguialday et al., 2019), while overall BCI’s are starting to prove their efficacy as rehabilitative technologies in patients with severe motor impairments (Chaudhary et al., 2016).

The feedback modalities used for BCI motor rehabilitation include: non-embodied simple two-dimensional tariffs on a screen (Prasad et al., 2010Mihara et al., 2013), embodied avatar representation of the patient on a screen or with augmented reality (Holper et al., 2010Pichiorri et al., 2015), neuromuscular electrical stimulation (NMES) (Kim et al., 2016Biasiucci et al., 2018). and robotic exoskeletal orthotic movement facilitation (Ramos-Murguialday et al., 2013Várkuti et al., 2013Ang et al., 2015). In addition, it has been shown that multimodal feedback lead to a significantly better performance in motor-imagery (Sollfrank et al., 2016) but also multimodal feedback combined with motor-priming, (Vourvopoulos and Bermúdez, 2016a). However, there is no evidence which modalities are more efficient in stroke rehabilitation are.

Taking into account all previous findings in the effects of multimodal feedback in MI training, the purpose of this case study is to examine the effect of the MI paradigm as a treatment for post-stroke upper limb motor dysfunction using the NeuRow BCI-VR system. This is achieved through the acquisition of clinical scales, dynamics of EEG during the BCI treatment, and brain activation as measured by functional MRI (fMRI). NeuRow is an immersive VR environment for MI-BCI training that uses an embodied avatar representation of the patient arms and haptic feedback. The combination of MI-BCIs with VR can reinforce activation of motor brain areas, by promoting the illusion of physical movement and the sense of embodiment in VR (Slater, 2017), and hence further engaging specific neural networks and mobilizing the desired neuroplastic changes. Virtual representation of body parts paves the way to include action observation during treatment. Moreover, haptic feedback is added since a combination of feedback modalities could prove to be more effective in terms of motor-learning (Sigrist et al., 2013). Therefore, the target of this system is to be used by patients with low or no levels of motor control. With this integrated BCI-VR approach, severe cases of stroke survivors may be admitted to a VR rehabilitation program, complementing traditional treatment.

Methodology

Patient Profile

In this pilot study we recruited a 60 years old male patient with left hemiparesis following cerebral infarct in the right temporoparietal region 10 months before. The participant had corrected vision through eyewear, he had 4 years of schooling and his experience with computers was reported as low. Moreover, the patient was on a low dose of diazepam (5 mg at night to help sleep), dual antiplatelet therapy, anti-hypertensive drug and metformin. Hemiparesis was associated with reduced dexterity and fine motor function; however, sensitivity was not affected. Other sequelae of the stroke included hemiparetic gait and dysarthria. Moreover, a mild cognitive impairment was identified which did not interfere with his ability to perform the BCI-VR training. The patient had no other relevant comorbidities. Finally, the patient was undergoing physiotherapy and occupational therapy at the time of recruitment and had been treated with botulinum toxin infiltration 2 months before due to focal spasticity of the biceps brachii.

Intervention Protocol

The patient underwent a 3-weeks intervention with NeuRow, resulting in 10 BCI sessions of a 15 min of exposure in VR training per session. Clinical scales, motor imagery capability assessment, and functional -together with structural- MRI data had been gathered in three time-periods: (1) before (serving as baseline), (2) shortly after the intervention and (3) one-month after the intervention (to assess the presence of long-term changes). Finally, electroencephalographic (EEG) data had been gathered during all sessions, resulting in more than 20 datasets of brain electrical activity.

The experimental protocol was designed in collaboration with the local healthcare system of Madeira, Portugal (SESARAM) and approved by the scientific and ethic committees of the Central Hospital of Funchal. Finally, written informed consent was obtained from the participant upon recruitment for participating to the study but also for the publication of the case report in accordance with the 1964 Declaration of Helsinki.

Assessment Tools

A set of clinical scales were acquired including the following:

1. Montreal Cognitive Assessment (MoCA). MoCA is a cognitive screening tool, with a score range between 0 and 30 (a score greater than 26 is considered to be normal) validated also for the Portuguese population, (Nasreddine et al., 2005).

2. Modified Ashworth scale (MAS). MAS is a 6-point rating scale for measuring spasticity. The score range is 0, 1, 1+, 2, 3, and 4 (Ansari et al., 2008).

3. Fugl-Meyer Assessment (FMA). FMA is a stroke specific scale that assesses motor function, sensation, balance, joint range of motion and joint pain. The motor domain for the upper limb has a maximum score of 66 (Fugl-Meyer et al., 1975).

4. Stroke Impact Scale (SIS). SIS is a subjective scale of the perceived stroke impact and recovery as reported by the patient, validated for the Portuguese population. The score of each domain of the questionnaire ranges from 0 to 100 (Duncan et al., 1999).

5. Vividness of Movement Imagery Questionnaire (VMIQ2). VMIQ2 is an instrument that assess the capability of the participant to perform imagined movements from external perspective (EVI), internal perspective imagined movements (IVI) and finally, kinesthetic imagery (KI) (Roberts et al., 2008).

NeuRow BCI-VR System

EEG Acquisition

For EEG data acquisition, the Enobio 8 (Neuroelectrics, Barcelona, Spain) system was used. Enobio is a wearable wireless EEG sensor with 8 EEG channels for the recording and visualization of 24-bit EEG data at 500 Hz and a triaxial accelerometer. The spatial distribution of the electrodes followed the 10–20 system configuration (Klem et al., 1999) with the following electrodes over the somatosensory and motor areas: Frontal-Central (FC5, FC6), Central (C1, C2, C3, C4), and Central-Parietal (CP5, CP6) (Figure 1A). The EEG system was connected via Bluetooth to a dedicated desktop computer, responsible for the EEG signal processing and classification, streaming the data via UDP through the Reh@Panel (RehabNet Control Panel) for controlling the virtual environment. The Reh@Panel is a free tool that acts as a middleware between multiple interfaces and virtual environments (Vourvopoulos et al., 2013).

FIGURE 1

Figure 1. Experimental setup, including: (A) the wireless EEG system; (B) the Oculus HMD, together with headphones reproducing the ambient sound from the virtual environment; (C) the vibrotactile modules supported by a custom-made table-tray, similar to the wheelchair trays used for support; (D) the visual feedback with NeuRow game. A written informed consent was obtained for the publication of this image.

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Continue —->  Frontiers | Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report | Frontiers in Human Neuroscience

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

Abstract

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

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.

References

  1. Wolpaw, J.R., Birbaumer, N., Heetderks, W.J. et al, Brain-computer interface technology: A review of the first international meeting. IEEE Trans Rehabil Eng2000;8:164–173.
  2. Kubanek, J., Miller, K., Ojemann, J., Wolpaw, J., Schalk, G. Decoding flexion of individual fingers using electrocorticographic signals in humans. J Neural Engin2009;6:066001.
  3. Arle JE, Shils JL, Malik WQ. Localized stimulation and recording in the spinal cord with microelectrode arrays. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE2012..

  4. Thakor, N.V. Translating the brain-machine interface. Sci Transl Med2013;5 (210ps217-210ps217).
  5. Irani, F., Platek, S.M., Bunce, S., Ruocco, A.C., Chute, D. Functional near infrared spectroscopy (fNIRS): An emerging neuroimaging technology with important applications for the study of brain disorders. Clin Neuropsychologist2007;21:9–37.
  6. Olson, J.D., Wander, J.D., Johnson, L. et al, Comparison of subdural and subgaleal recordings of cortical high-gamma activity in humans. Clin Neurophysiol2016;127:277–284.
  7. Olson, J.D., Wander, J.D., Darvas, F. Demonstration of motor-related beta and high gamma brain signals in subdermal electroencephalography recordings. Clin Neurophysiol2017;128:395–396.
  8. Schalk, G., Wolpaw, J.R., McFarland, D.J., Pfurtscheller, G. EEG-based communication: Presence of an error potential. Clin Neurophysiol2000;111:2138–2144.
  9. McFarland, D.J., McCane, L.M., Wolpaw, J.R. EEG-based communication and control: Short-term role of feedback. IEEE Trans Rehabil Eng1998;6:7–11.
  10. Widge, A.S., Moritz, C.T., Matsuoka, Y. Direct neural control of anatomically correct robotic hands. Brain-Computer Interfaces. Springer-VerlagBerlin, Heidelberg2010:105–119.
  11. Fetz, E.E. Volitional control of neural activity: Implications for brain–computer interfaces. J Physiol2007;579:571–579.
  12. Miller, K.J., Schalk, G., Fetz, E.E., Den Nijs, M., Ojemann, J.G., Rao, R.P. Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proc Natl Acad Sci USA2010;107:4430–4435.
  13. Bouton, C.E., Shaikhouni, A., Annetta, N.V. et al, Restoring cortical control of functional movement in a human with quadriplegia. Nature2016;533:247–250.
  14. Sharma, G., Friedenberg, D.A., Annetta, N. et al, Using an artificial neural bypass to restore cortical control of rhythmic movements in a human with quadriplegia. Sci Rep2016;6:33807.
  15. Collinger, J.L., Boninger, M.L., Bruns, T.M., Curley, K., Wang, W., Weber, D.J. Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury. J Rehabil Res Dev2013;50:145–160.
  16. Wodlinger, B., Downey, J.E., Tyler-Kabara, E.C., Schwartz, A.B., Boninger, M.L., Collinger, J.L. Ten-dimensional anthropomorphic arm control in a human brain-machine interface: Difficulties, solutions, and limitations. J Neural Eng2015;12:016011.
  17. Friedenberg, D.A., Schwemmer, M.A., Landgraf, A.J. et al, Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human. Sci Rep2017;7:8386.
  18. Ajiboye, A.B., Willett, F.R., Young, D.R. et al, Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration. Lancet2017;389:1821–1830.
  19. Batula, A.M., Mark, J.A., Kim, Y.E., Ayaz, H. Comparison of brain activation during motor imagery and motor movement using fNIRS. Comput Intell Neurosci2017;2017:5491296.
  20. Wu J, Casimo K, Caldwell DJ, Rao RP, Ojemann JG. Electrocorticographic dynamics predict visually guided motor imagery of grasp shaping. Paper presented at: Neural Engineering (NER), 2017 8th International IEEE/EMBS Conference on, 2017..

  21. Flesher, S., Downey, J., Collinger, J. et al, Intracortical microstimulation as a feedback source for brain-computer interface users. in: C. Guger, B. Allison, J. Ushiba (Eds.) Brain-Computer Interface ResearchSpringer International PublishingBasel2017:43–54.
  22. Luan, S., Williams, I., Nikolic, K., Constandinou, T.G. Neuromodulation: Present and emerging methods. Front Neuroeng2014;7:27.
  23. Nardone, R., Höller, Y., Taylor, A. et al, Noninvasive spinal cord stimulation: Technical aspects and therapeutic applications. Neuromodulation2015;18:580–591.
  24. Cronin, J.A., Wu, J., Collins, K.L. et al, Task-specific somatosensory feedback via cortical stimulation in humans. IEEE Trans Haptics2016;9:515–522.
  25. Collins, K.L., Guterstam, A., Cronin, J., Olson, J.D., Ehrsson, H.H., Ojemann, J.G. Ownership of an artificial limb induced by electrical brain stimulation. Proc Natl Acad Sci USA2017;114:166–171.
  26. O’Doherty, J.E., Lebedev, M.A., Ifft, P.J. et al, Active tactile exploration using a brain–machine–brain interface. Nature2011;479:228.
  27. Hiremath, S.V., Tyler-Kabara, E.C., Wheeler, J.J. et al, Human perception of electrical stimulation on the surface of somatosensory cortex. PLoS One2017;12:e0176020.
  28. Venkatakrishnan, A., Francisco, G.E., Contreras-Vidal, J.L. Applications of brain–machine interface systems in stroke recovery and rehabilitation. Curr Phys Med Rehabil Rep2014;2:93–105.
  29. Friedenberg, D.A., Bouton, C.E., Annetta, N.V. et al, Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface. Conf Proc IEEE Eng Med Biol Soc2016;:3084–3087.
  30. Friedenberg DA, Schwemmer M, Skomrock N, et al. Neural decoding algorithm requirements for a take-home brain computer interface. Conf Proc IEEE Eng Med Biol Soc, 2018, in press..

  31. Downey, J.E., Weiss, J.M., Muelling, K. et al, Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping. J Neuroeng Rehabil2016;13:28.
  32. Knutson, J.S., Fu, M.J., Sheffler, L.R., Chae, J. Neuromuscular electrical stimulation for motor restoration in hemiplegia. Phys Med Rehabil Clin N Am2015;26:729.
  33. Ragnarsson, K. Functional electrical stimulation after spinal cord injury: Current use, therapeutic effects and future directions. Spinal Cord2008;46:255.
  34. Peckham, P.H., Keith, M.W., Kilgore, K.L. et al, Efficacy of an implanted neuroprosthesis for restoring hand grasp in tetraplegia: A multicenter study. Arch Phys Med Rehabil2001;82:1380–1388.
  35. Pool, D., Elliott, C., Bear, N. et al, Neuromuscular electrical stimulation-assisted gait increases muscle strength and volume in children with unilateral spastic cerebral palsy. Dev Med Child Neurol2016;58:492–501.
  36. Mulcahey, M.J., Betz, R.R., Kozin, S., Smith, B.T., Hutchinson, D., Lutz, C. Implantaton of the Freehand system during initial rehabilitation using minimally invasive techniques. Spinal Cord2004;42:146–155.
  37. Lauer, R.T., Peckham, P.H., Kilgore, K.L. EEG-based control of a hand grasp neuroprosthesis.Neuroreport1999;10:1767–1771.
  38. Müller-Putz, G.R., Scherer, R., Pfurtscheller, G., Rupp, R. EEG-based neuroprosthesis control: A step towards clinical practice. Neurosci Lett2005;382:169–174.
  39. Pfurtscheller, G., Müller, G.R., Pfurtscheller, J., Gerner, H.J., Rupp, R. ‘Thought’—control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett2003;351:33–36.
  40. Rohm, M., Schneiders, M., Müller, C. et al, Hybrid brain–computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury. Artif Intell Med2013;59:133–142.
  41. Rupp, R., Rohm, M., Schneiders, M. et al, Think2grasp-bci-controlled neuroprosthesis for the upper extremity. Biomed Tech (Berl)2013; (https://doi.org/10.1515/bmt-2013-4440).
  42. Grimm, F., Walter, A., Spüler, M., Naros, G., Rosenstiel, W., Gharabaghi, A. Hybrid neuroprosthesis for the upper limb: Combining brain-controlled neuromuscular stimulation with a multi-joint arm exoskeleton. Front Neurosci2016;10:367.
  43. Burke, D., Gorman, E., Stokes, D., Lennon, O. An evaluation of neuromuscular electrical stimulation in critical care using the ICF framework: A systematic review and meta-analysis. Clin Respir J2016;10:407–420.
  44. Stein, C., Fritsch, C.G., Robinson, C., Sbruzzi, G., Plentz, R.D.M. Effects of electrical stimulation in spastic muscles after stroke: Systematic review and meta-analysis of randomized controlled trials.Stroke2015;46:2197–2205.
  45. Marquez-Chin, C., Marquis, A., Popovic, M.R. EEG-triggered functional electrical stimulation therapy for restoring upper limb function in chronic stroke with severe hemiplegia. Case Rep Neurol Med2016;2016:9146213.
  46. Rodrıguez, M., Pierre, C., Couve, S. et al, Towards brain–robot interfaces in stroke rehabilitation.PLoS One2011;6:1–17.
  47. Takahashi, M., Takeda, K., Otaka, Y. et al, Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: A feasibility study. J Neuroeng Rehabil2012;9:56.
  48. Knaut, L.A., Subramanian, S.K., McFadyen, B.J., Bourbonnais, D., Levin, M.F. Kinematics of pointing movements made in a virtual versus a physical 3-dimensional environment in healthy and stroke subjects. Arch Phys Med Rehabil2009;90:793–802.
  49. Laver, K.E., Lange, B., George, S., Deutsch, J.E., Saposnik, G., Crotty, M. Virtual reality for stroke rehabilitation. Stroke2018; (STROKEAHA.117.020275).
  50. Collinger, J.L., Wodlinger, B., Downey, J.E. et al, High-performance neuroprosthetic control by an individual with tetraplegia. Lancet2013;381:557–564.
  51. Tidoni, E., Abu-Alqumsan, M., Leonardis, D. et al, Local and remote cooperation with virtual and robotic agents: A P300 BCI study in healthy and people living with spinal cord injury. IEEE Trans Neural Syst Rehabil Eng2017;25:1622–1632.
  52. Colachis, S.C. IV, Bockbrader, M.A., Zhang, M. et al, Dexterous control of seven functional hand movements using cortically-controlled transcutaneous muscle stimulation in a person with tetraplegia. Front Neurosci2018;12:208.
  53. Saleh, S., Fluet, G., Qiu, Q., Merians, A., Adamovich, S.V., Tunik, E. Neural patterns of reorganization after intensive robot-assisted virtual reality therapy and repetitive task practice in patients with chronic stroke. Front Neurol2017;8:452.
  54. Fluet G, Patel J, Qinyin Q, et al. Early versus delayed VR-based hand training in persons with acute stroke. Paper presented at: 2017 International Conference on Virtual Rehabilitation (ICVR); June 19-22, 2017..

  55. Pfurtscheller, G., Guger, C., Müller, G., Krausz, G., Neuper, C. Brain oscillations control hand orthosis in a tetraplegic. Neurosci Lett2000;292:211–214.
  56. Lee, K., Liu, D., Perroud, L., Chavarriaga, R., Millán, JdR. A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers. Robotics Autonomous Systems2017;90:15–23.
  57. Sakurada, T., Kawase, T., Takano, K., Komatsu, T., Kansaku, K. A BMI-based occupational therapy assist suit: Asynchronous control by SSVEP. Front Neurosci2013;7:172.
  58. Chen, G., Chan, C.K., Guo, Z., Yu, H. A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. Crit Rev Biomed Eng2013;41:343–363.
  59. Louie, D.R., Eng, J.J. Powered robotic exoskeletons in post-stroke rehabilitation of gait: A scoping review. J Neuroeng Rehabil2016;13:53.
  60. Veerbeek, J.M., Langbroek-Amersfoort, A.C., van Wegen, E.E., Meskers, C.G., Kwakkel, G. Effects of robot-assisted therapy for the upper limb after stroke: A systematic review and meta-analysis.Neurorehabil Neural Repair2017;31:107–121.
  61. McConnell, A.C., Moioli, R.C., Brasil, F.L. et al, Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke. J Rehabil Med2017;49:449–460.
  62. Pfurtscheller, G., Allison, B., Bauernfeind, G. et al, The hybrid BCI. Front Neurosci2010;4:30.
  63. Galán, F., Nuttin, M., Lew, E. et al, A brain-actuated wheelchair: Asynchronous and non-invasive brain–computer interfaces for continuous control of robots. Clin Neurophysiol2008;119:2159–2169.
  64. Raspopovic, S., Capogrosso, M., Petrini, F.M. et al, Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci Transl Med2014;6 (222ra219-222ra219).
  65. Flesher, S.N., Collinger, J.L., Foldes, S.T. et al, Intracortical microstimulation of human somatosensory cortex. Sci Transl Med2016;8 (361ra141-361ra141).
  66. Jezernik, S., Colombo, G., Keller, T., Frueh, H., Morari, M. Robotic orthosis lokomat: A rehabilitation and research tool. Neuromodulation2003;6:108–115.
  67. Daly, J.J., Wolpaw, J.R. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol2008;7:1032–1043.
  68. Dobkin, B.H. Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol2007;579:637–642.
  69. Bamdad, M., Zarshenas, H., Auais, M.A. Application of BCI systems in neurorehabilitation: A scoping review. Disabil Rehabil2015;10:355–364.
  70. Sellers, E.W., Ryan, D.B., Hauser, C.K. Noninvasive brain-computer interface enables communication after brainstem stroke. Sci Transl Med2014;6 (257re257-257re257).
  71. Wang, F., He, Y., Qu, J. et al, Enhancing clinical communication assessments using an audiovisual BCI for patients with disorders of consciousness. J Neural Engin2017;14:046024.
  72. Hochberg, L.R., Serruya, M.D., Friehs, G.M. et al, Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature2006;442:164–171.
  73. Tankus, A., Yeshurun, Y., Flash, T., Fried, I. Encoding of speed and direction of movement in the human supplementary motor area. J Neurosurg2009;110:1304–1316.
  74. Wang Y, Hong B, Gao X, Gao S. Phase synchrony measurement in motor cortex for classifying single-trial EEG during motor imagery. Paper presented at: Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE2006..

  75. Hermes, D., Vansteensel, M.J., Albers, A.M. et al, Functional MRI-based identification of brain areas involved in motor imagery for implantable brain–computer interfaces. J Neural Engin2011;8:025007.
  76. Wang, W., Collinger, J.L., Degenhart, A.D. et al, An electrocorticographic brain interface in an individual with tetraplegia. PLoS One2013;8:e55344.
  77. Wang Y, Makeig S. Predicting intended movement direction using EEG from human posterior parietal cortex. Paper presented at: International Conference on Foundations of Augmented Cognition, 2009..

  78. Klaes, C., Kellis, S., Aflalo, T. et al, Hand shape representations in the human posterior parietal cortex. J Neurosci2015;35:15466–15476.
  79. Broetz, D., Braun, C., Weber, C., Soekadar, S.R., Caria, A., Birbaumer, N. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: A case report.Neurorehabil Neural Repair2010;24:674–679.
  80. Ramos-Murguialday, A., Broetz, D., Rea, M. et al, Brain–machine interface in chronic stroke rehabilitation: A controlled study. Ann Neurol2013;74:100–108.
  81. Ang, K.K., Guan, C., Phua, K.S. et al, 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 Neuroeng2014;7:30.
  82. Heasman, J., Scott, T., Kirkup, L., Flynn, R., Vare, V., Gschwind, C. Control of a hand grasp neuroprosthesis using an electroencephalogram-triggered switch: Demonstration of improvements in performance using wavepacket analysis. Med Biol Eng Comput2002;40:588–593.
  83. Onose, G., Grozea, C., Anghelescu, A. et al, On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: A clinical test and long-term post-trial follow-up. Spinal Cord2012;50:599.
  84. Kreilinger A, Kaiser V, Rohm M, Rupp R, Müller-Putz GR. BCI and FES training of a spinal cord injured end-user to control a neuroprosthesis. Biomed Tech (Berl), 2013. https://doi.org/10.1515/bmt-2013-4443..

  85. Downey, J.E., Brane, L., Gaunt, R.A., Tyler-Kabara, E.C., Boninger, M.L., Collinger, J.L. Motor cortical activity changes during neuroprosthetic-controlled object interaction. Sci Rep2017;7:16947.
  86. Kennedy, P.R., Bakay, R.A. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport1998;9:1707–1711.
  87. Spataro, R., Chella, A., Allison, B. et al, Reaching and grasping a glass of water by locked-In ALS patients through a BCI-controlled humanoid robot. Front Hum Neurosci2017;11:68.
  88. Hochberg, L.R., Bacher, D., Jarosiewicz, B. et al, Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature2012;485:372–375.
  89. Keith, M.W., Peckham, P.H., Thrope, G.B., Buckett, J.R., Stroh, K.C., Menger, V. Functional neuromuscular stimulation neuroprostheses for the tetraplegic hand. Clin Orthop Relat Res1988;233:25–33.
  90. Bockbrader, M., Kortes, M.J., Annetta, N. et al, Implanted brain-computer interface controlling a neuroprosthetic for increasing upper limb function in a human with tetraparesis. PM R2016;8:S242–S243.
  91. Wang, P.T., King, C.E., Chui, L.A., Do, A.H., Nenadic, Z. Self-paced brain–computer interface control of ambulation in a virtual reality environment. J Neural Engin2012;9:056016.
  92. Louie, D.R., Eng, J.J., Lam, T. Gait speed using powered robotic exoskeletons after spinal cord injury: A systematic review and correlational study. J Neuroeng Rehabil2015;12:82.
  93. He Y, Nathan K, Venkatakrishnan A, et al. An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE2014..

  94. Osuagwu, B.C., Wallace, L., Fraser, M., Vuckovic, A. Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: A randomised pilot study. J Neural Engin2016;13:065002.
  95. Proietti, T., Crocher, V., Roby-Brami, A., Jarrassé, N. Upper-limb robotic exoskeletons for neurorehabilitation: a review on control strategies. IEEE Rev Biomed Engin2016;9:4–14.
  96. Donati, A.R., Shokur, S., Morya, E. et al, Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Sci Rep2016;6:30383.
  97. Buch, E., Weber, C., Cohen, L.G. et al, Think to move: A neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke2008;39:910–917.
  98. Caria, A., Weber, C., Brötz, D. et al, Chronic stroke recovery after combined BCI training and physiotherapy: A case report. Psychophysiology2011;48:578–582.
  99. Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci2012;6:39.
  100. Várkuti, B., Guan, C., Pan, Y. et al, Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair2013;27:53–62.
  101. Zondervan, D.K., Augsburger, R., Bodenhoefer, B., Friedman, N., Reinkensmeyer, D.J., Cramer, S.C.Machine-based, self-guided home therapy for individuals with severe arm impairment after stroke: A randomized controlled trial. Neurorehabil Neural Repair2015;29:395–406.
  102. Chen, Y.-P., Howard, A.M. Effects of robotic therapy on upper-extremity function in children with cerebral palsy: A systematic review. Dev Neurorehabil2016;19:64–71.
  103. Dolbow, J.D., Mehler, C., Stevens, S.L., Hinojosa, J. Robotic-assisted gait training therapies for pediatric cerebral palsy: A review. J Rehabil Robotics2016;4:14–21.
  104. Hu, X.L., Tong, K.-y., Song, R., Zheng, X.J., Leung, W.W. A comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke. Neurorehabil Neural Repair2009;23:837–846.
  105. Young, B.M., Nigogosyan, Z., Walton, L.M. et al, Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface. Front Neuroeng2014;7:26.
  106. Sullivan JL, Bhagat NA, Yozbatiran N, et al. Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial. Paper presented at: Rehabilitation Robotics (ICORR), 2017 International Conference, 2017..

  107. Johansson, R.S., Flanagan, J.R. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat Rev Neurosci2009;10:345.
  108. Monzée, J., Lamarre, Y., Smith, A.M. The effects of digital anesthesia on force control using a precision grip. J Neurophysiol2003;89:672–683.
  109. Johansson, R., Hager, C., Backstrom, L. Somatosensory control of precision grip during unpredictable pulling loads III. Impairments during digital anaesthesia. Exp Brain Res1992;89:204–213.
  110. Vaso, A., Adahan, H.-M., Gjika, A. et al, Peripheral nervous system origin of phantom limb pain.Pain2014;155:1384–1391.
  111. Polikandriotis, J.A., Hudak, E.M., Perry, M.W. Minimally invasive surgery through endoscopic laminotomy and foraminotomy for the treatment of lumbar spinal stenosis. J Orthop2013;10:13–16.
  112. Alimi, M., Njoku, I. Jr., Cong, G.-T. et al, Minimally invasive foraminotomy through tubular retractors via a contralateral approach in patients with unilateral radiculopathy. Op Neurosurg2014;10:436–447.
  113. Pirris, S.M., Dhall, S., Mummaneni, P.V., Kanter, A.S. Minimally invasive approach to extraforaminal disc herniations at the lumbosacral junction using an operating microscope: Case series and review of the literature. Neurosurg Focus2008;25:E10.
  114. Cruccu, G., Aziz, T., Garcia-Larrea, L. et al, EFNS guidelines on neurostimulation therapy for neuropathic pain. Eur J Neurol2007;14:952–970.
  115. Liem, L., Russo, M., Huygen, F.J. et al, A multicenter, prospective trial to assess the safety and performance of the spinal modulation dorsal root ganglion neurostimulator system in the treatment of chronic pain. Neuromodulation2013;16:471–482.
  116. Deer, T.R., Grigsby, E., Weiner, R.L., Wilcosky, B., Kramer, J.M. A prospective study of dorsal root ganglion stimulation for the relief of chronic pain. Neuromodulation2013;16:67–72.
  117. Eldabe, S., Burger, K., Moser, H. et al, Dorsal root ganglion (DRG) stimulation in the treatment of phantom limb pain (PLP). Neuromodulation2015;18:610–617.
  118. Lynch, P.J., McJunkin, T., Eross, E., Gooch, S., Maloney, J. Case report: Successful epiradicular peripheral nerve stimulation of the C2 dorsal root ganglion for postherpetic neuralgia.Neuromodulation2011;14:58–61.
  119. Tan, D., Tyler, D., Sweet, J., Miller, J. Intensity modulation: A novel approach to percept control in spinal cord stimulation. Neuromodulation2016;19:254–259.
  120. Tan, D.W., Schiefer, M.A., Keith, M.W., Anderson, J.R., Tyler, J., Tyler, D.J. A neural interface provides long-term stable natural touch perception. Sci Transl Med2014;6 (257ra138-257ra138).
  121. Viswanathan, A., Phan, P.C., Burton, A.W. Use of spinal cord stimulation in the treatment of phantom limb pain: Case series and review of the literature. Pain Practice2010;10:479–484.
  122. Berg, J., Dammann, J. III, Tenore, F. et al, Behavioral demonstration of a somatosensory neuroprosthesis. IEEE Trans Neural Syst Rehabil Eng2013;21:500–507.
  123. Kim, S., Callier, T., Tabot, G.A., Gaunt, R.A., Tenore, F.V., Bensmaia, S.J. Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex. Proc Natl Acad Sci USA2015;112:15202–15207.
  124. Kim, S., Callier, T., Tabot, G.A., Tenore, F.V., Bensmaia, S.J. Sensitivity to microstimulation of somatosensory cortex distributed over multiple electrodes. Front Syst Neurosci2015;9:47.
  125. Callier, T., Schluter, E.W., Tabot, G.A., Miller, L.E., Tenore, F.V., Bensmaia, S.J. Long-term stability of sensitivity to intracortical microstimulation of somatosensory cortex. J Neural Engin2015;12:056010.
  126. Yuste, R., Goering, S., Agüera y Arcas, B. et al, Four ethical priorities for neurotechnologies and AI.Nature2017;551:159–163.
  127. Klein, E. Informed consent in implantable BCI research: Identifying risks and exploring meaning.Sci Engin Ethics2016;22:1299–1317.

 

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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

Badan Penelitian dan Pengembangan Kesehatan. Riset Kesehatan Dasar 2013, Available at : http://www.depkes.go.id/resources/download/general/Hasil%20Riskesdas%202013.pdf, 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 : https://www.emotiv.com, accessed June 2017

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

B. Shoelson. edfRead, Available at : https://www.mathworks.com/matlabcentral/fileexchange/ 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.

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