Posts Tagged BCI

[ARTICLE] Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis – Full Text

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.

Background

Motor deficit is the most common sequela after stroke, resulting in severe negative impacts on activities of daily living and social participation for patients [1]. Spontaneous recovery usually occurs within the first 3 months after the onset of stroke; however, there exists a great deal of variability in recovery across patients, particularly patients with severe deficits, who tend to recover less and more slowly [2]. With regard to the importance of motor training in facilitating motor recovery after stroke, various rehabilitation training protocols, such as task-specific training and constrained-induced motor training have been applied in regard to stroke [34]. However, these protocols are limited in patients with severe motor function deficit, due to the voluntary participation of hemiparetic hands. On the other hand, brain-computer interface (BCI) technology does not involve the direct volitional control of hemiparetic hands in training; therefore, it may be promising for these patients.

The term “BCIs” refers to systems that capture the features of brain activity and translate them into computerized commands to control external devices, which can be communication devices [5], functional electrical stimulation (FES) [6], or exoskeleton robots [7], among others. To acquire brain activity signals, either invasive or non-invasive strategies can be used. Invasive BCIs can acquire spatiotemporal signals and have a great capacity to distinguish more dimensions of patients’ intent through implants in the brain cortex [8]. However, non-invasive BCIs, using signals collected from electroencephalography (EEG), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), or functional magnetic resonance imaging (fMRI), may be more promising than the invasive strategy in reality, due to safety and ethical issues [9]. Among them, the EEG signal-based BCI is the most commonly used system because of its relatively simple and inexpensive equipment requirements, as well as rich sources regarding its temporal resolution (e.g., visually evoked potential, P300, slow cortical potential) and frequency (e.g., power in given frequency bands) domains, the information can be extracted as the feature for controlling external devices [10]. The EEG signal-based BCI captures the signal of the event-related and time-locked decrease or increase in the oscillatory power in given frequency bands; in other words, the event-related desynchronization (ERD) or event-related synchronization (ERS), respectively [1112]. At present, hybrid BCI systems that combine more than one signal can provide more efficient natural control of external devices [13].[…]

Contimue —–> https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-020-00686-2

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[Abstract] Combining brain–computer interface and virtual reality for rehabilitation in neurological diseases: A narrative review

Abstract

Background

The traditional rehabilitation for neurological diseases lacks the active participation of patients, its process is monotonous and tedious, and the effects need to be improved. Therefore, a new type of rehabilitation technology with more active participation combining brain–computer interface (BCI) with virtual reality (VR) has developed rapidly in recent years and has been used in rehabilitation in neurological diseases.

Objectives

This narrative review analyzed and characterized the development and application of the new training system (BCI-VR) in rehabilitation of neurological diseases from the perspective of the BCI paradigm, to provide a pathway for future research in this field.

Methods

The review involved a search of the Web of Science-Science Citation Index/Social Sciences Citation Index and the China National Knowledge Infrastructure databases; 39 papers were selected. Advantages and challenges of BCI-VR – based neurological rehabilitation were analyzed in detail.

Results

Most BCI-VR studies included could be classified by 3 major BCI paradigms: motor imagery, P300, and steady-state visual-evoked potential. Integrating VR scenes into BCI systems could effectively promote the recovery process from nervous system injuries as compared with traditional methods.

Conclusion

As compared with rehabilitation based on traditional BCI, rehabilitation based on BCI-VR can provide better feedback information for patients and promote the recovery of brain function. By solving the challenges and continual development, the BCI-VR system can be broadly applied to the clinical treatment of various neurological diseases.

Source: https://www.sciencedirect.com/science/article/abs/pii/S1877065720301184?dgcid=rss_sd_all&utm_campaign=RESR_MRKT_Researcher_inbound&utm_medium=referral&utm_source=researcher_app

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[ARTICLE] Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation – Full Text HTML

Abstract

Sensorimotor rhythm (SMR)-based brain–computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conducted to develop and validate an SMR-based BCI-FES system with 2-class MI tasks in a single hand (Phase 1), and investigate the feasibility of the system with stroke and traumatic brain injury (TBI) patients (Phase 2). The results of Phase 1 showed that the accuracy of classifying 2-class MIs (approximately 71.25%) was significantly higher than the true chance level, while that of distinguishing voluntary and passive SMRs was not. In Phase 2, where the patients performed goal-oriented tasks in a semi-asynchronous mode, the effects of the FES existence type and adaptive learning on task performance were evaluated. The results showed that adaptive learning significantly increased the accuracy, and the accuracy after applying adaptive learning under the No-FES condition (61.9%) was significantly higher than the true chance level. The outcomes of the present research would provide insight into SMR-based BCI-controlled FES systems that can connect those with motor disabilities (e.g., stroke and TBI patients) to other people by greatly improving their quality of life. Recommendations for future work with a larger sample size and kinesthetic MI were also presented.

1. Introduction

Healthy individuals whose brains and neuromuscular systems enable normal motor functions can naturally perform Activities of Daily Living (ADLs). Nonetheless, for some people who have disabilities in these functions due to injury or disease, simple tasks become very difficult or impossible to do. To assist this population, researchers in many fields, from physical therapy to engineering, have developed various rehabilitation technologies that help them perform ADLs [1,2]. One such technology, Functional Electrical Stimulation (FES), delivers electrical impulses to either paralyzed or impaired limbs to generate artificial muscle contraction [3,4]. In this way, FES helps disabled people perform ADLs such as walking, reaching, and grasping [5,6]. Some FES devices are controlled by brain–computer interfaces (BCIs), sometimes called brain–machine interfaces.
In general, BCIs can help people communicate and control devices and applications without using peripheral nerves and muscle pathways [7]. BCIs are also a potential method to promote the independence of physically disabled people by means of the BCI’s ability to bypass non-functional neural pathways [8]. A sensorimotor rhythm (SMR)-based BCI-controlled FES system is a novel technology that combines the advantages of FES and BCI systems, and allows severely disabled patients to restore motor functions through the FES system by translating voluntary Motor Imagery (MI) to physical action [9]. There are many potential benefits of combining SMR-based BCIs and FES systems, such as the promotion of neuroplasticity [10], the restoration of motor functions by using voluntary motor intentions [9,11], and providing proprioceptive sensory feedback as a result of their intentions [12].
Although SMR-based BCI-controlled FES methods seem promising, current studies still have central issues: (1) ambiguous instruction of MI tasks during training under SMR-based BCI systems, and (2) difficulties in classifying voluntary MI-evoked SMRs and FES-driven passive-movement-evoked SMRs when FES is activated. Moreover, (3) only a few studies have examined the feasibility of classifying two different MI tasks of a single hand, such as grasping and opening, and (4) few studies have examined human factors and ergonomics (HF/E) perspectives such as subjective mental workload and user satisfaction in the use of SMR-based BCI-controlled FES systems. This research that is composed of two phases was conducted to address these issues by developing a new SMR-based BCI system with visual guidance during training to classify a 2-class MI task in a single hand, as well as voluntary and passive SMRs (Phase 1), and evaluating the feasibility of the proposed BCI-controlled FES system by performing sequential goal-oriented tasks with stroke and TBI patients (Phase 2).
The remainder of this article consists of five more sections (this introduction being Section 1): Section 2 describes a survey of current SMR-based BCI studies for FES systems to identify the limitations of current research and clarifies the current state of BCI-controlled FES technologies. Section 3 presents Phase 1, where an SMR-based BCI system to control FES was developed and validated to address the issues on current research studies. Section 4 describes Phase 2, which assessed the feasibility of the proposed BCI-FES system by conducting a sequential task with fixed order under a semi-asynchronous mode. Section 5 discusses the findings of the present research along with implications and future directions.[…]

Continue —-> Brain Sciences | Free Full-Text | Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation | HTML

Figure 1. Schematic illustration of the experiment procedure. Text in the blue box indicates the auditory cue that played at the beginning of each period, and INI is an abbreviation of the Functional Electrical Stimulation (FES) initiation period. MI: Motor Imagery.

 

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[ARTICLE] The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response – Full Text

Abstract

Background

Stroke is a leading cause of long-term disability. Cost-effective post-stroke rehabilitation programs for upper limb are critically needed. Brain-Computer Interfaces (BCIs) which enable the modulation of Electroencephalography (EEG) sensorimotor rhythms are promising tools to promote post-stroke recovery of upper limb motor function. The “Promotoer” study intends to boost the application of the EEG-based BCIs in clinical practice providing evidence for a short/long-term efficacy in enhancing post-stroke hand functional motor recovery and quantifiable indices of the participants response to a BCI-based intervention. To these aims, a longitudinal study will be performed in which subacute stroke participants will undergo a hand motor imagery (MI) training assisted by the Promotoer system, an EEG-based BCI system fully compliant with rehabilitation requirements.

Methods

This longitudinal 2-arm randomized controlled superiority trial will include 48 first ever, unilateral, subacute stroke participants, randomly assigned to 2 intervention groups: the BCI-assisted hand MI training and a hand MI training not supported by BCI. Both interventions are delivered (3 weekly session; 6 weeks) as add-on regimen to standard intensive rehabilitation. A multidimensional assessment will be performed at: randomization/pre-intervention, 48 h post-intervention, and at 1, 3 and 6 month/s after end of intervention. Primary outcome measure is the Fugl-Meyer Assessment (FMA, upper extremity) at 48 h post-intervention. Secondary outcome measures include: the upper extremity FMA at follow-up, the Modified Ashworth Scale, the Numeric Rating Scale for pain, the Action Research Arm Test, the National Institute of Health Stroke Scale, the Manual Muscle Test, all collected at the different timepoints as well as neurophysiological and neuroimaging measures.

Discussion

We expect the BCI-based rewarding of hand MI practice to promote long-lasting retention of the early induced improvement in hand motor outcome and also, this clinical improvement to be sustained by a long-lasting neuroplasticity changes harnessed by the BCI-based intervention. Furthermore, the longitudinal multidimensional assessment will address the selection of those stroke participants who best benefit of a BCI-assisted therapy, consistently advancing the transfer of BCIs to a best clinical practice.

Trial registration

Name of registry: BCI-assisted MI Intervention in Subacute Stroke (Promotoer).

Trial registration number: NCT04353297; registration date on the ClinicalTrial.gov platform: April, 15/2020.

Peer Review reports

Background

Stroke is a major public health and social care concern worldwide [1]. The upper limb motor impairment commonly persists after stroke, and it represents the major contribution to long-term disability [2]. It has been estimated that the main clinical predictor of whether a patient would come back to work is the degree of upper extremity function [3]. Despite the intensive rehabilitation, the variability in the nature and extent of upper limb recovery remains a crucial factor affecting rehabilitation outcomes [4].

Electroencephalography (EEG)-based Brain-Computer Interface (BCI) is an emerging technology that enables a direct translation of brain activity into motor action [5]. Recently, EEG-based BCIs have been recognized as potential tools to promote functional motor recovery of upper limbs after stroke (for review see [6]). Several randomized controlled trials have shown that stroke patients can learn to modulate their EEG sensorimotor rhythms [7] to control external devices and this practice might facilitate neurological recovery both in subacute and chronic stroke phase [8,9,10].

We were previously successful in the design and validation of an EEG sensorimotor rhythms–based BCI combined with realistic visual feedback of upper limb to support hand motor imagery (MI) practice in stroke patients [1112]. Our previous pilot randomized controlled study [8] with the participation of 28 subacute stroke patients with severe motor deficit, suggested that 1 month BCI-assisted MI practice as an add-on intervention to the usual rehabilitation care was superior with respect to the add-on, 1 month MI training alone (ie., without BCI support) in improving hand functional motor outcomes (indicated by the significantly higher mean score at upper extremity Fugl-Meyer scale in the BCI with respect to control group). A greater involvement of the ipsilesional hemisphere, as reflected by a stronger motor-related EEG oscillatory activity and connectivity in response to MI of the paralyzed trained hand was also observed only in the BCI-assisted MI training condition. These promising findings corroborated the idea that a relatively low-cost technique (i.e. EEG-based BCI) can be exploited to deliver an efficacious rehabilitative intervention such as MI training and prompted us to undertake a translational effort by implementing an all-in-one BCI-supported MI training station– the Promotoer [13].

Yet, important questions remain to be addressed in order to improve the clinical viability of BCIs such as defining whether the expected early improvements in functional motor outcomes induced by the BCI-assisted MI training in subacute stroke [8] can be sustained in a long-term as it has been shown for other BCI-based approaches in chronic stroke patients [1014]. This requires advancements in the knowledge on brain functional re-organization early after stroke and on how this re-organization would correlate with the functional motor outcome (evidence-base medicine). Last but not least, the definition of the determinants of the patients response to treatment is paramount to optimize the process of personalized medicine in rehabilitation. We will address these questions by carrying out a randomized trial to eventually establish the fundamentals for a cost-effective use of EEG-based BCI technology to deliver a rehabilitative intervention such as the MI in hospitalized stroke patients.

Aim and hypotheses

The “Promotoer” study is a randomized controlled trial (RCT) designed to provide evidence for a significant early improvement of hand motor function induced by the BCI-assisted MI training operated via the Promotoer and for a persistency (up to 6 months) of such improvement. Task-specific training was reported to induce long-term improvements in arm motor function after stroke [15,16,17]. Thus, our hypothesis is that the BCI-based rewarding of hand MI tasks would promote long-lasting retention of early induced positive effect on motor performance with respect to MI tasks practiced in an open loop condition (ie, without BCI). Accordingly, the primary aim of the “Promotoer” RCT will be first to determine whether the BCI based intervention (MI-BCI) administered by means of a BCI system fully compatible with a clinical setting (the Promotoer), is superior to a non-BCI assisted MI training (MI Control) in improving hand motor function outcomes in sub-acute stroke patients admitted to the hospital for their standard rehabilitation care; secondly, we will test whether the efficacy of BCI-based intervention on hand motor function outcomes is sustained long-term after the end of intervention (6 months follow-up). A further hypothesis is that such clinical improvement would be sustained by a long-lasting neuroplasticity changes as harnessed by the BCI–based intervention. This hypothesis rises from current evidence for an early enhancement of post-stroke plastic changes enabled by BCI-based trainings [8,9,10]. To test this hypothesis, a longitudinal assessment of the brain network organization derived from advanced EEG signal processing (secondary objective) will be performed.

The heterogeneity of stroke makes prediction of treatment responders a great challenge [18]. The potential value of a combination of neurophysiological and neuroimaging biomarkers with the clinical assessment in predicting post-stroke motor recovery has been recently highlighted [19]. Our hypothesis is that the longitudinal combined functional, neurophysiological and neuroimaging assessment over 6 months from the intervention will allow for insights into biomarkers and potential predictors of patients response to the BCI-Promotoer training (secondary aim). To this purpose, well-recognized factors contributing to recovery after stroke such as the relation between clinical profile, lesion characteristics and patterns of post-stroke motor cortical re-organization (eg., ipsilesional/contralesional primary and non-primary motor areas, cortico-spinal tract integrity, severity of motor deficits at baseline; for review see [19]) will be taken into account.[…}

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figure2
The Promotoer system. The Promoter is equipped with a computer, a commercial wireless EEG/EMG system (g.MOBIlab, g.tec medical engineering GmbH Austria), a screen for the therapist feedback (for the electroencephalographic – EEG activity and electromyographic- EMG activity monitoring) and screen for the ecological feedback to the participant; this ecological feedback is delivered by means of a custom software program that provides for (personalized) visual representation of the participant’s own hands. As such, this software allows the therapists to create an artificial reproduction of a given participant’s hand and forearm by adjusting a digitally created image in shape, size, skin color and orientation to match as much as possible the real hand and arm of the participant. Real-time feedback is provided by means of BCI2000 software [40]. The degree of EEG desynchronization over selected electrodes within selected frequencies (BCI control features) determines the vertical velocity of the cursor on the therapist’s screen and it operates the “virtual” hand software accordingly. The image is original as it is owned by the authors

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[ARTICLE] Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis – Full Text

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.

Background

Motor deficit is the most common sequela after stroke, resulting in severe negative impacts on activities of daily living and social participation for patients [1]. Spontaneous recovery usually occurs within the first 3 months after the onset of stroke; however, there exists a great deal of variability in recovery across patients, particularly patients with severe deficits, who tend to recover less and more slowly [2]. With regard to the importance of motor training in facilitating motor recovery after stroke, various rehabilitation training protocols, such as task-specific training and constrained-induced motor training have been applied in regard to stroke [34]. However, these protocols are limited in patients with severe motor function deficit, due to the voluntary participation of hemiparetic hands. On the other hand, brain-computer interface (BCI) technology does not involve the direct volitional control of hemiparetic hands in training; therefore, it may be promising for these patients.

The term “BCIs” refers to systems that capture the features of brain activity and translate them into computerized commands to control external devices, which can be communication devices [5], functional electrical stimulation (FES) [6], or exoskeleton robots [7], among others. To acquire brain activity signals, either invasive or non-invasive strategies can be used. Invasive BCIs can acquire spatiotemporal signals and have a great capacity to distinguish more dimensions of patients’ intent through implants in the brain cortex [8]. However, non-invasive BCIs, using signals collected from electroencephalography (EEG), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), or functional magnetic resonance imaging (fMRI), may be more promising than the invasive strategy in reality, due to safety and ethical issues [9]. Among them, the EEG signal-based BCI is the most commonly used system because of its relatively simple and inexpensive equipment requirements, as well as rich sources regarding its temporal resolution (e.g., visually evoked potential, P300, slow cortical potential) and frequency (e.g., power in given frequency bands) domains, the information can be extracted as the feature for controlling external devices [10]. The EEG signal-based BCI captures the signal of the event-related and time-locked decrease or increase in the oscillatory power in given frequency bands; in other words, the event-related desynchronization (ERD) or event-related synchronization (ERS), respectively [1112]. At present, hybrid BCI systems that combine more than one signal can provide more efficient natural control of external devices [13].

In 2009, Daly et al. [14] reported the first case study concerning the feasibility of an EEG signal-based BCI combined with FES in regard to stroke rehabilitation. After a three-week training period, the patient under study regained volitional isolated index finger extension, suggesting the potential immediate effects of this method on motor recovery [14]. In subsequent well-designed studies, the immediate effects of BCIs on motor function were confirmed [1516] and researchers also explored the immediate effects on improvements in spasticity [15], muscle strength [16], and activities of daily living [1617]. However, many well-known rehabilitation strategies, such as virtual reality [18] and mirror therapy [19], which showed superior immediate effects, might not have long-term effects across time. The latest meta-analysis summarized the immediate clinical effects of BCIs based on nine studies; the overall results support the effectiveness of BCI training on the improvement of upper extremity motor function in stroke [20]. However, the evidence related to the immediate effects of BCIs in other aspects (e.g., spasticity, strength, etc.) and corresponding long-term effects were not certain.

At present, brain activity during motor imagery (MI) and movement attempts can be used to trigger external devices. However, it is believed that these two mental tasks have different mechanisms in regard to promoting neural plasticity. MI is a mental rehearsal of movements without any real movement. The neural substrates of MI have been extensively studied with neuroimaging techniques and have been found to possess substantial overlapping with the neural network of motor execution, such as in the contralateral supplementary motor area (SMA), contralateral postcentral gyrus, contralateral superior parietal lobe, and ipsilateral prefrontal cortex [2122]. On the other hand, it is well known that the mu (8–13 Hz) and beta (13–30 Hz) rhythms over the primary motor cortex (M1) and bilaterally across the precentral motor cortex desynchronize during motor execution, movement attempts, and MI [2324]. A study using electrocorticography shows that both motor execution and MI induced ERD in mu and beta bands accompanied by ERS at high frequencies (76–100 Hz) over contralateral M1, but the former had larger changes than the latter [8]. Transcranial magnetic stimulation (TMS) further proved the enhanced cortical excitability of M1 during MI, as measured by increased motor-evoked potential (MEP) [25]. In 2010, Prasad et al. reported on the use of an MI-based BCI system in regard to five patients with chronic stroke; their results show the proof-of-concept of BCI training in regard to improving motor function [26].

In addition to MI, movement attempts (i.e., patients attempt to move their paretic hands, even though they have completely lost voluntary movements) have been proposed for BCIs in stroke [14]. A previous neuroimaging study indicated that the cortical activity of movement attempts closely followed the somatotopic organization of motor execution in patients after spinal cord injuries [27]. The neural mechanism of movement attempt-based BCIs refers to Hebbian plasticity, which is different from that of MI. Hebbian plasticity explains a form of enhanced synaptic plasticity if a close timing order of pre- and post-synaptic activity occurs [28]. Post-synaptic spiking after presynaptic firing can result in short-term potentiation, which is largely dependent on the N-methyl-D-aspartate receptor [29]; the sensorimotor loop is disrupted in patients with stroke due to the loss of voluntary movements, but the capacity of motor planning may still be retained. A previous study indicated that movement attempts could be extracted from EEGs for patients with complete hand paralysis [30] and can be used to trigger external devices (e.g., robot arms), potentially restoring the normal timing order of motor preparation, execution, and peripheral muscle effectors [30]. Therefore, through this form of BCI training, patients could learn to control the brain oscillatory activity induced by movement attempts through immediate and correct somatosensory feedback, and a new sensorimotor loop could be established [1516]. Recently, researchers have argued that movement attempts are more informative than MI, because patients have to actively suppress the movement of extremities in MI, while it is more natural to attempt movement [31].

To establish a closed sensorimotor loop, BCIs are combined with different external devices to achieve feedback regarding self-regulated brain activity. FES has been used in BCI systems to elicit muscle contraction in the paretic arm, by delivering electrical stimulation [32]. It has been proven that FES is able to facilitate the efficacy of closed sensorimotor loop during BCI training, by increasing the patient’s movement awareness during motor training and by enhancing corticospinal excitability [33]. Robots (e.g., exoskeletons and orthosis) have also been integrated in BCI systems to provide proprioceptive feedback. The clinical effects of robot-assisted therapy were found to be modest in comparison with conventional rehabilitation, according to the results of a large-scale study [34]. However, when integrated with BCI training, patients can control their movements with the assistance of robotic devices more voluntarily, thus improving their participation [15]. In addition, visual feedback is used in BCI training to provide simple and fast feedback regarding brain activity [35]. As indicated in the review conducted by van Dokkum et al. [36], different external devices appear to play different roles in the closed sensorimotor loop. For instance, BCIs combined with FES can link movement intention with muscle contraction, turning the bottom-up approach of FES into a top-down approach. Moreover, a study carried out by Ono et al. [37] indicated that the external device providing proprioceptive feedback tended to be more effective than visual feedback in clinical outcomes, suggesting that external devices may significantly boost the effects of BCIs. To the best of our knowledge, there have been no studies directly comparing the effects of different external devices combined with BCI training in clinical outcomes.

Anodal stimulation of transcranial direct current stimulation (tDCS), is capable of exciting the cortex [38]. Recent studies have found it effective in increasing the ERD of mu rhythm during MI [39], and thereby improved motor performance when combined with BCI training based on MI tasks [40]. Although the clinical effects of BCIs in stroke can be potentiated by a preceding tDCS to the cortex, the effects of tDCS in facilitating BCI applications, in regard to restoring motor function for stroke, have not been reviewed before.

A recent meta-analysis by Cervera et al. [20] evaluated the immediate effects of BCIs on the improvement of upper extremity motor function for stroke. The current study aims: (1) to investigate both the immediate and long-term clinical effects of BCI training on the improvement of hemiparetic upper extremity function, and the related neural plasticity changes elicited by BCIs in patients after stroke; (2) to study the potential differences in treatment effects caused by different training paradigms for BCIs measuring signals from the motor cortex (e.g., MI-based BCIs and movement attempt-based BCIs); (3) to explore the potential differential effects of BCIs when combined with different kinds of external devices; and (4) to explore the potentiating effect of tDCS on BCI training.[…]

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[Abstract] Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application

Highlights

• BCI methods are among the most effective tool for designing rehabilitation systems

.• Use of virtual reality (VR) can increase the efficiency of BCI rehab systems

.• “FES,” “Robotics Assistance,” and “Hybrid VR based Models” are main BCI approaches.

• In the future, flexible electronics can be used for designing stroke rehab systems.

Abstract

Strokes are a growing cause of mortality and many stroke survivors suffer from motor impairment as well as other types of disabilities in their daily life activities. To treat these sequelae, motor imagery (MI) based brain-computer interface (BCI) systems have shown potential to serve as an effective neurorehabilitation tool for post-stroke rehabilitation therapy. In this review, different MI-BCI based strategies, including “Functional Electric Stimulation, Robotics Assistance and Hybrid Virtual Reality based Models,” have been comprehensively reported for upper-limb neurorehabilitation. Each of these approaches have been presented to illustrate the in-depth advantages and challenges of the respective BCI systems. Additionally, the current state-of-the-art and main concerns regarding BCI based post-stroke neurorehabilitation devices have also been discussed. Finally, recommendations for future developments have been proposed while discussing the BCI neurorehabilitation systems.

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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.

 

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.

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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.

 

via The Integration of Brain-Computer Interface (BCI) as Control Module for Functional Electrical Stimulation (FES) Intervention in Post-Stroke Upper Extremity Rehabilitation

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