Posts Tagged Brain Computer Interface

[Short Review] NCyborg Project – A new stroke rehabilitation pattern based on brain computer interface

Abstract

NCyborg Project, a new stroke rehabilitation pattern based on brain computer interface (BCI) and brain-inspired intelligent robots, is set up by Tongji Hospital and BrainCo. We will briefly introduce this project in this paper.

NCyborg Project, a new stroke rehabilitation pattern based on brain computer interface (BCI) and brain-inspired intelligent robots, is set up by Tongji Hospital and BrainCo.

In China, stroke is the leading cause of death and disability in the adult population, with 1.96 million deaths each year, and 75% of the surviving patients will lose the ability to move independently. Most patients with physical disabilities cannot take care of themselves, and are difficult to carry out daily activities independently, therefore, requiring long-term functional exercises and rehabilitation.

Traditional stroke rehabilitation equipment only allows patients to passively follow the movements of the equipment. Hence, the rehabilitation effect is poor, and the patient’s willingness to train is also low. It can only be utilized as an auxiliary means for rehabilitation by the practitioners, thus increasing the cost of the rehabilitation treatment.

The department of Neurology in Tongji Hospital, affiliated to Tongji Medical College, Huazhong University of Science and Technologyis in a leading position in China in terms of scientific research and clinical strength. It undertakes 54 national, provincial and municipal research projects. The current research projects include 2 Major Special Projects of the Ministry of Science and Technology, 5 National Key R&D Programs, 1 Major Research Cultivation Program of the National Natural Science Foundation of China, 28 General Programs and Distinguished Young Scholars Programs of National Natural Science Foundation of China, and 13 provincial and municipal research projects. In the past three years, Tongji Hospital has obtained 5 various scientific research achievement awards and 19 Chinese invention patents. It has significantly influenced the clinical and basic research of neurology in China and overseas and has had a profound impact on the related textbooks, books and clinical guidelines. Meanwhile, the number of outpatient clinics in the department of Neurology reached more than 210,000 cases in 2020, the majority of which were stroke patients.

Zhejiang BrainCo, Ltd. (www.brainco.cn), incubated by Harvard Innovation Lab (www.innovationlabs.harvard.edu), is in a leading position in brain-computer interfaces, which is known as the next generation of artificial intelligence technology. BrainCo has more than 100 core patents in the field of brain-computer interface. In the “2019 Artificial Intelligence Development Whitepaper”1 released by the China Academy of Science, BrainCo, as the only brain-computer interface company on the list, was selected as the World’s Top 20 Artificial Intelligence Companies. Its intelligent BrainRobotics Prosthetic Hand based on the brain-computer interface technology was named the “Best Invention of 2019”2 by Time Magazine in the United States.

In the NCyborg Project, Tongji Hospital and BrainCo will cooperate to use brain-computer interface technology and brain-inspired intelligent robot technology to realize the rehabilitation process driven by the patients’ initiative and improve the treatment effect of stroke survivors, see Fig. 1.

The cooperative research will be carried out from the following three aspects:

(1) An algorithm for analyzing the movement intention of stroke patients based on brain-computer interface technology. Stroke survivors often experience damage to the central nervous system after a stroke, so that their movement intention cannot be effectively transmitted to the peripheral nervous system and muscles. Through the brain-computer interface technology, the patients’ active intention can be obtained from the damaged nerve tissue of the patients, thereby bypassing the damaged nerve-muscle pathway and realizing the transmission of the movement intention.

(2) Motion control strategy of rehabilitation robot based on brain-inspired motion perception. The purpose is to allow rehabilitation robots to adapt to complex and changeable activities of daily life (ADLs), to achieve the dual role of function rehabilitation and aids to daily living, to endow rehabilitation robots with the capability of brain-inspired motion perception, to realize the perception of the surrounding environment information, and to reduce users’ attention burden.

(3) The mechanism of stroke rehabilitation of brain-inspired intelligent robots. Neuroplasticity and motion function reorganization of brain tissue are the neurological principles of stroke rehabilitation. But the rehabilitation involves not only the recovery of the nervous system, but also the rehabilitation of the blood circulatory system and the muscular system. Hence, we need to study the interactive between neuromuscular and perivascular systems from micro-scale, macro-scale and meso-scale perspectives.

In a word, NCyborg Project aims to develop an easy-to-use, reliable and affordable stroke rehabilitation robot to improve the rehabilitation effect of stroke survivors, speed up the rehabilitation process, and reduce the costs. The robot will first start with hand rehabilitation and is expected to realize the recognition of no less than eight hand movement intentions with the recognition accuracy of ≥ 90% and the response time ≤ 300 ms. Additionally, we are looking forward to, within five years, letting millions of stroke patients use this product with their lives improved after stoke.

In NCyborg ProjectN means Neural and Cyborg means a system of biological and technical mixed type. In fictional stories, Cyborg is often claimed as icon that is enhanced mentally and/or physically over and above the “norm” with technology. In the real word, we believe that NCyborg Project based on brain computer interface and brain-inspired intelligent robots will set up a brand new stroke rehabilitation pattern which could qualitatively improve the treatment effect of stroke survivors.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

1 Online access website: https://bdk.ucas.ac.cn/index.php/xyxw/2780-20190113.Google Scholar

2 Online access website: https://time.com/collection/best-inventions-2019/5733081/brainrobotics-ai-prosthetic-hand/.Google Scholar

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[WEB] Neurotechnology holds promise for chronic stroke patients

Neurotechnology holds promise for chronic stroke patients
Brain-computer interface — EEG cap. Credit: Wyss Center, wysscenter.ch

Wyss Center for Bio and Neuroengineering, Geneva, Switzerland—Personalized neurotechnology-aided rehabilitation of the arm could improve recovery in severe chronic stroke patients according to a new paper published today in the journal Brain.

Neurotechnology-based therapies, including brain-machine interfaces, robotics, and brain stimulation among others, will lead to largest treatment effects and success if they are tailored to the needs of individual patients, and used in combination, say the authors from the Wyss Center for Bio and Neuroengineering, Swiss Federal Institute of Technology Lausanne (EPFL), Scuola Superiore Sant’Anna, University of Geneva Faculty of Medicine and Clinique Romande de Réadaptation. The paper calls for longitudinal clinical studies to show the rehabilitation benefits of individual therapies as well as the use of multiple complementary therapies used in combination over long time periods.

Lead author Dr. Martina Coscia, Staff Engineer at the Wyss Center said: “Our findings show that neurotechnology-aided upper limb rehabilitation is promising for severe chronic stroke patients. However, we also found that the ‘one size fits all’ approach doesn’t lead to the best outcome. We suggest a move towards a personalized combination of neurotechnology-based stroke rehabilitation therapies, ideally in a home-based environment where prolonged therapy is more feasible than in a clinic.

Neurotechnology holds promise for chronic stroke patients
Personalized neurotechnology-aided rehabilitation of the arm could improve recovery in severe chronic stroke patients according to a new paper published today in the journal Brain. Credit: Wyss Center wysscenter.ch

“We believe that by sequentially introducing stroke therapies according to individual progress, we could allow patients to continue their recovery beyond what is possible today.”

Stroke is a major public health problem that causes long-term impairments in millions of people worldwide. One of the most common consequences of stroke is impaired upper arm function which has a direct impact on daily tasks and quality of life. Rehabilitation therapies generally have the largest effect in the first three months after stroke. After this time, patients are considered chronic and the likelihood of further natural recovery is limited; this is especially true for those most severely affected.

Professor Friedhelm Hummel from EPFL (Director, Defitech Chair of Clinical Neuroengineering) and the University of Geneva Medical School said: “What we would like to see in the future are long-term trials in which multiple neurotechnology-based therapies are used within the same patient. We believe that this synergistic approach could uncover previously undiscovered treatment pathways for chronic stroke patients.”

Neurotechnology holds promise for chronic stroke patients
Neurotechnology-based interventions for upper limb motor rehabilitation in severe chronic stroke. Credit: Wyss Center, wysscenter.ch

The authors compared effectiveness data from 64 clinical studies on upper limb neurotechnology-aided treatments in chronic stroke patients. The interventions analyzed in the paper included robotics, functional electrical stimulation of muscles, brain stimulation and brain-computer interfaces as well as their use in combination.

The interdisciplinary research team is now starting a clinical trial to test these ideas. The trial uses a new experimental design with a personalized therapy approach using brain-computer interfaces, robotics, functional electrical stimulation and brain stimulation specifically chosen to maximize treatment effects in each individual patient. The goal is to keep incrementally improving recovery by using new personalized, neurotechnology-based therapies in combination. The trial will start in Switzerland in summer 2019.


Explore further How to help patients recover after a stroke


More information: Martina Coscia et al. Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke, Brain (2019). DOI: 10.1093/brain/awz181 Journal information:Brain

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[Abstract] Combined action observation- and motor imagery-based brain computer interface (BCI) for stroke rehabilitation: a case report – Full text PDF

Abstract 


Introduction:

Upper extremity impairment is a problem usually found in poststroke patients, and it is seldom completely improved even following conventional physical therapy. Motor imagery (MI) and action observation (AO) therapy are mental practices that may regain motor function in poststroke patients, especially when integrating them with brain-computer interface (BCI) technology. However, previous studies have always investigated the effects of an MI- or AO-based BCI for stroke rehabilitation separately. Therefore, in this study, we aimed to propose the effectiveness of a combined AO and MI (AOMI)-based BCI with functional electrical stimulation (FES) feedback to improve upper limb functions and alter brain activity patterns in chronic stroke patients. Case presentation: A 53-year-old male who was 12 years post stroke was left hemiparesis and unable to produce any wrist and finger extension. Intervention: The participant was given an AOMI-based BCI with FES feedback 3 sessions per week for 4 consecutive weeks, and he did not receive any conventional physical therapy during the intervention. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) and active range of motion (AROM) of wrist extension were used as clinical assessments, and the laterality coefficient (LC) value was applied to explore the altered brain activity patterns affected by the intervention. Outcomes: The FMA-UE score improved from 34 to 46 points, and the AROM of wrist extension was increased from 0 degrees to 20 degrees. LC values in the alpha band tended to be positive whereas LC values in the beta band seemed to be slightly negative after the intervention.

Conclusion:

An AOMI-based BCI with FES feedback training may be a promising strategy that could improve motor function in poststroke patients; however, its efficacy should be studied in a larger population and compared to that of other therapeutic methods.

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[POSTER Abstract] Rehabilitation With Brain Computer Interface to Improve Hand Function After Stroke

Abstract

Background: Stroke is the third cause of disability and the first cause of motor impairment of the upper limb, which significantly reduces independence in activities of daily living. Therefore, the development and use of new therapeutic approaches is needed. Brain-computer interfaces (BCI) based on movement intention (MI) have the potential to improve hand function and brain plasticity after stroke.

Purpose: Improve hand function after stroke with BCI with MI.

Methods: 8 patients with subacute stroke, left or right hemiparesis was divided in two groups. The first received conventional therapy followed by BCI-MI therapy and the second was given BCI-MI therapy and then conventional therapy. The outcome variables were sensory-motor recovery, hand function, spasticity, level of disability, grip strength, change in motor threshold and amplitude of motor evoked potentials (MEP) and changes in desynchronization/synchronization event-related (EDR/EDS).

Results: 4 patients were female, the average age 56 ± 12.7 years, and 87% were ischemic stroke. No differences were found between post-conventional therapy and postBCI-MI in the sensorimotor recovery, the spasticity, and the degree of deficiency in the strength. Only in hand function measured with the Action Research Arm Test (ARAT), a statistically significant difference was found in the BCI-MI therapy group (p= 0.0247). Dynamometry grip strength had a tendency to increase the Post-BCI group. A MEP was obtained in the ipsilesional primary motor cortex of 3 patients. A higher EDR was found in the injured hemisphere in favor of the post-BCI group (p= 0.0165).

Conclusions: This study shows that BCI-MI rehabilitation, which combines MI with a robotic hand orthosis as feedback, has a significant improvement in hand function and a greater ERD in the ipsilesional primary motor cortex. It is necessary to carry out controlled clinical trials with a larger sample and with a greater frequency and duration of intervention to measure hand motor and functional improvement of patients after stroke.

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[ARTICLE] BCI Training Effects on Chronic Stroke Correlate with Functional Reorganization in Motor-Related Regions: A Concurrent EEG and fMRI Study – Full Text PDF

Abstract

Brain–computer interface (BCI)-guided robot-assisted training strategy has been increasingly applied to stroke rehabilitation, while few studies have investigated the neuroplasticity change and functional reorganization after intervention from multimodality neuroimaging perspective. The present study aims to investigate the hemodynamic and electrophysical changes induced by BCI training using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) respectively, as well as the relationship between the neurological changes and motor function improvement. Fourteen chronic stroke subjects received 20 sessions of BCI-guided robot hand training.
Simultaneous EEG and fMRI data were acquired before and immediately after the intervention. Seed-based functional connectivity for resting-state fMRI data and effective connectivity analysis for EEG were processed to reveal the neuroplasticity changes and interaction between different brain regions. Moreover, the relationship among motor function improvement, hemodynamic changes, and electrophysical changes derived from the two neuroimaging modalities was also investigated.
This work suggested that (a) significant motor function improvement could be obtained after BCI training therapy, (b) training effect significantly correlated with functional connectivity change between ipsilesional M1 (iM1) and contralesional Brodmann area 6 (including premotor area (cPMA) and supplementary motor area (SMA)) derived from fMRI, (c) training effect significantly correlated with information flow change from cPMA to iM1 and strongly correlated with information flow change from SMA to iM1 derived from EEG, and (d) consistency of fMRI and EEG results illustrated by the correlation between functional connectivity change and information flow change. Our study showed changes in the brain after the BCI training therapy from chronic stroke survivors and provided a better understanding of neural mechanisms, especially the interaction among motor-related brain regions during stroke recovery. Besides, our finding demonstrated the feasibility and consistency of combining multiple neuroimaging modalities to investigate the neuroplasticity change.[…]

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

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

Introduction

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

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

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

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

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

Methods

Subjects

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

Subject Preparation

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

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

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[Review] Immediate and long-term effects of BCIbased rehabilitation of the upper extremity after stroke: a systematic review and metaanalysis – Full Text PDF

Abstract

Background: A substantial number of clinical studies have demonstrated the functional recovery induced by the use of brain-computer interface (BCI) technology in patients after stroke. The objective of this review is to evaluate the effect sizes of clinical studies investigating the use of BCIs in restoring upper extremity function after stroke and
the potentiating effect of transcranial direct current stimulation (tDCS) on BCI training for motor recovery.

Methods: The databases (PubMed, Medline, EMBASE, CINAHL, CENTRAL, PsycINFO, and PEDro) were systematically searched for eligible single-group or clinical controlled studies regarding the effects of BCIs in hemiparetic upper extremity recovery after stroke. Single-group studies were qualitatively described, but only controlled-trial studies were included in the meta-analysis. The PEDro scale was used to assess the methodological quality of the controlled studies. A meta-analysis of upper extremity function was performed by pooling the standardized mean difference (SMD). Subgroup meta-analyses regarding the use of external devices in combination with the application of BCIs were also carried out. We summarized the neural mechanism of the use of BCIs on stroke.

Results: A total of 1015 records were screened. Eighteen single-group studies and 15 controlled studies were included. The studies showed that BCIs seem to be safe for patients with stroke. The single-group studies consistently showed a
trend that suggested BCIs were effective in improving upper extremity function. The meta-analysis (of 12 studies) showed a medium effect size favoring BCIs for improving upper extremity function after intervention (SMD = 0.42; 95% CI = 0.18–0.66; I2 = 48%; P < 0.001; fixed-effects model), while the long-term effect (five studies) was not significant (SMD = 0.12; 95% CI = − 0.28 – 0.52; I2 = 0%; P = 0.540; fixed-effects model). A subgroup meta-analysis indicated that using functional electrical stimulation as the external device in BCI training was more effective than using other devices (P = 0.010). Using movement attempts as the trigger task in BCI training appears to be more effective than using motor
imagery (P = 0.070). The use of tDCS (two studies) could not further facilitate the effects of BCI training to restore upper extremity motor function (SMD = − 0.30; 95% CI = − 0.96 – 0.36; I2 = 0%; P = 0.370; fixed-effects model).

Conclusion: The use of BCIs has significant immediate effects on the improvement of hemiparetic upper extremity function in patients after stroke, but the limited number of studies does not support its long-term effects. BCIs combined with functional electrical stimulation may be a better combination for functional recovery than other kinds
of neural feedback. The mechanism for functional recovery may be attributed to the activation of the ipsilesional premotor and sensorimotor cortical network.

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

Abstract

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

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[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.

 

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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.

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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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