EMG- Versus EEG-Triggered Electrical Stimulation for Inducing Corticospinal Plasticity

A Brain Computer Interface Neuromodulatory Device for Stroke Rehabilitation: Iterative User-Centered Design Approach

BACKGROUND

Rehabilitation technologies for people with stroke are rapidly evolving. These technologies have the potential to support higher volumes of rehabilitation to improve outcomes for people with stroke. Despite growing evidence of their efficacy, there is a lack of uptake and sustained use in stroke rehabilitation and a call for user-centered design approaches during technology design and development. This study focuses on a novel rehabilitation technology called exciteBCI, a complex neuromodulatory wearable technology in the prototype stage that augments locomotor rehabilitation for people with stroke. The exciteBCI consists of a brain computer interface, a muscle electrical stimulator, and a mobile app.

OBJECTIVE

This study presents the evaluation phase of an iterative user-centered design approach supported by a qualitative descriptive methodology that sought to (1) explore users' perspectives and experiences of exciteBCI and how well it fits with rehabilitation, and (2) facilitate modifications to exciteBCI design features.

METHODS

The iterative usability evaluation of exciteBCI was conducted in 2 phases. Phase 1 consisted of 3 sprint cycles consisting of single usability sessions with people with stroke (n=4) and physiotherapists (n=4). During their interactions with exciteBCI, participants used a "think-aloud" approach, followed by a semistructured interview. At the end of each sprint cycle, device requirements were gathered and the device was modified in preparation for the next cycle. Phase 2 focused on a "near-live" approach in which 2 people with stroke and 1 physiotherapist participated in a 3-week program of rehabilitation augmented by exciteBCI (n=3). Participants completed a semistructured interview at the end of the program. Data were analyzed from both phases using conventional content analysis.

RESULTS

Overall, participants perceived and experienced exciteBCI positively, while providing guidance for iterative changes. Five interrelated themes were identified from the data: (1) "This is rehab" illustrated that participants viewed exciteBCI as having a good fit with rehabilitation practice; (2) "Getting the most out of rehab" highlighted that exciteBCI was perceived as a means to enhance rehabilitation through increased engagement and challenge; (3) "It is a tool not a therapist," revealed views that the technology could either enhance or disrupt the therapeutic relationship; and (4) "Weighing up the benefits versus the burden" and (5) "Don't make me look different" emphasized important design considerations related to device set-up, use, and social acceptability.

CONCLUSIONS

This study offers several important findings that can inform the design and implementation of rehabilitation technologies. These include (1) the design of rehabilitation technology should support the therapeutic relationship between the patient and therapist, (2) social acceptability is a design priority in rehabilitation technology but its importance varies depending on the use context, and (3) there is value in using design research methods that support understanding usability in the context of sustained use.

Attentional State-Dependent Peripheral Electrical Stimulation During Action Observation Enhances Cortical Activations in Stroke Patients

Brain-computer interface (BCI) is a promising technique that enables patients' interaction with computers or machines by analyzing specific brain signal patterns and provides patients with brain state-dependent feedback to assist in their rehabilitation. Action observation (AO) and peripheral electrical stimulation (PES) are conventional methods used to enhance rehabilitation outcomes by promoting neural plasticity. In this study, we assessed the effects of attentional state-dependent feedback in the combined application of BCI-AO with PES on sensorimotor cortical activation in patients after stroke. Our approach involved showing the participants a video with repetitive grasping actions under four different tasks. A mu band suppression (8-13 Hz) corresponding to each task was computed. A topographical representation showed that mu suppression of the dominant (healthy) and affected hemispheres (stroke) gradually became prominent during the tasks. There were significant differences in mu suppression in the affected motor and frontal cortices of the stroke patients. The involvement of both frontal and motor cortices became prominent in the BCI-AO+triggered PES task, in which feedback was given to the patients according to their attentive watching. Our findings suggest that synchronous stimulation according to patient attention is important for neurorehabilitation of stroke patients, which can be achieved with the combination of BCI-AO feedback with PES. BCI-AO feedback combined with PES could be effective in facilitating sensorimotor cortical activation in the affected hemispheres of stroke patients.

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017-2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed.

Implementing Performance Accommodation Mechanisms in Online BCI for Stroke Rehabilitation: A Study on Perceived Control and Frustration

Brain-computer interfaces (BCIs) are successfully used for stroke rehabilitation, but the training is repetitive and patients can lose the motivation to train. Moreover, controlling the BCI may be difficult, which causes frustration and leads to even worse control. Patients might not adhere to the regimen due to frustration and lack of motivation/engagement. The aim of this study was to implement three performance accommodation mechanisms (PAMs) in an online motor imagery-based BCI to aid people and evaluate their perceived control and frustration. Nineteen healthy participants controlled a fishing game with a BCI in four conditions: (1) no help, (2) augmented success (augmented successful BCI-attempt), (3) mitigated failure (turn unsuccessful BCI-attempt into neutral output), and (4) override input (turn unsuccessful BCI-attempt into successful output). Each condition was followed-up and assessed with Likert-scale questionnaires and a post-experiment interview. Perceived control and frustration were best predicted by the amount of positive feedback the participant received. PAM-help increased perceived control for poor BCI-users but decreased it for good BCI-users. The input override PAM frustrated the users the most, and they differed in how they wanted to be helped. By using PAMs, developers have more freedom to create engaging stroke rehabilitation games.

An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study

Many studies have used motor imagery-based brain–computer interface (MI-BCI) systems for stroke rehabilitation to induce brain plasticity. However, they mainly focused on detecting motor imagery but did not consider the effect of false positive (FP) detection. The FP could be a threat to patients with stroke as it can induce wrong-directed brain plasticity that would result in adverse effects. In this study, we proposed a rehabilitative MI-BCI system that focuses on rejecting the FP. To this end, we first identified numerous electroencephalogram (EEG) signals as the causes of the FP, and based on the characteristics of the signals, we designed a novel two-phase classifier using a small number of EEG channels, including the source of the FP. Through experiments with eight healthy participants and nine patients with stroke, our proposed MI-BCI system showed 71.76% selectivity and 13.70% FP rate by using only four EEG channels in the patient group with stroke. Moreover, our system can compensate for day-to-day variations for prolonged session intervals by recalibration. The results suggest that our proposed system, a practical approach for the clinical setting, could improve the therapeutic effect of MI-BCI by reducing the adverse effect of the FP.

Brain-computer interface-based action observation combined with peripheral electrical stimulation enhances corticospinal excitability in healthy subjects and stroke patients

Objective.Action observation (AO) combined with brain-computer interface (BCI) technology enhances cortical activation. Peripheral electrical stimulation (PES) increases corticospinal excitability, thereby activating brain plasticity. To maximize motor recovery, we assessed the effects of BCI-AO combined with PES on corticospinal plasticity.Approach.Seventeen patients with chronic hemiplegic stroke and 17 healthy subjects were recruited. The participants watched a video of repetitive grasping actions with four different tasks for 15 min: (A) AO alone; (B) AO + PES; (C) BCI-AO + continuous PES; and (D) BCI-AO + triggered PES. PES was applied at the ulnar nerve of the wrist. The tasks were performed in a random order at least three days apart. We assessed the latency and amplitude of motor evoked potentials (MEPs). We examined changes in MEP parameters pre-and post-exercise across the four tasks in the first dorsal interosseous muscle of the dominant hand (healthy subjects) and affected hand (stroke patients).Main results.The decrease in MEP latency and increase in MEP amplitude after the four tasks were significant in both groups. The increase in MEP amplitude was sustained for 20 min after tasks B, C, and D in both groups. The increase in MEP amplitude was significant between tasks A vs. B, B vs. C, and C vs. D. The estimated mean difference in MEP amplitude post-exercise was the highest for A and D in both groups.Significance.The results indicate that BCI-AO combined with PES is superior to AO alone or AO + PES for facilitating corticospinal plasticity in both healthy subjects and patients with stroke. Furthermore, this study supports the idea that synchronized activation of cortical and peripheral networks can enhance neuroplasticity after stroke. We suggest that the BCI-AO paradigm and PES could provide a novel neurorehabilitation strategy for patients with stroke.

Associative cued asynchronous BCI induces cortical plasticity in stroke patients

OBJECTIVE

We propose a novel cue-based asynchronous brain-computer interface(BCI) for neuromodulation via the pairing of endogenous motor cortical activity with the activation of somatosensory pathways.

METHODS

The proposed BCI detects the intention to move from single-trial EEG signals in real time, but, contrary to classic asynchronous-BCI systems, the detection occurs only during time intervals when the patient is cued to move. This cue-based asynchronous-BCI was compared with two traditional BCI modes (asynchronous-BCI and offline synchronous-BCI) and a control intervention in chronic stroke patients. The patients performed ankle dorsiflexion movements of the paretic limb in each intervention while their brain signals were recorded. BCI interventions decoded the movement attempt and activated afferent pathways via electrical stimulation. Corticomotor excitability was assessed using motor-evoked potentials in the tibialis-anterior muscle induced by transcranial magnetic stimulation before, immediately after, and 30 min after the intervention.

RESULTS

The proposed cue-based asynchronous-BCI had significantly fewer false positives/min and false positives/true positives (%) as compared to the previously developed asynchronous-BCI. Linear-mixed-models showed that motor-evoked potential amplitudes increased following all BCI modes immediately after the intervention compared to the control condition (p <0.05). The proposed cue-based asynchronous-BCI resulted in the largest relative increase in peak-to-peak motor-evoked potential amplitudes(141% ± 33%) among all interventions and sustained it for 30 min(111% ± 33%).

INTERPRETATION

These findings prove the high performance of a newly proposed cue-based asynchronous-BCI intervention. In this paradigm, individuals receive precise instructions (cue) to promote engagement, while the timing of brain activity is accurately detected to establish a precise association with the delivery of sensory input for plasticity induction.

A 28 nm Bulk CMOS Fully Digital BPSK Demodulator for US-Powered IMDs Downlink Communications

Low-invasive and battery-less implantable medical devices (IMDs) have been increasingly emerging in recent years. The developed solutions in the literature often concentrate on the Bidirectional Data-Link for long-term monitoring devices. Indeed, their ability to collect data and communicate them to the external world, namely Data Up-Link, has revealed a promising solution for bioelectronic medicine. Furthermore, the capacity to control organs such as the brain, nerves, heart-beat and gastrointestinal activities, made up through the manipulation of electrical transducers, could optimise therapeutic protocols and help patients’ pain relief. These kinds of stimulations come from the modulation of a powering signal generated from an externally placed unit coupled to the implanted receivers for power/data exchanging. The established communication is also defined as a Data Down-Link. In this framework, a new solution of the Binary Phase-Shift Keying (BPSK) demodulator is presented in this paper in order to design a robust, low-area, and low-power Down-Link for ultrasound (US)-powered IMDs. The implemented system is fully digital and PLL-free, thus reducing area occupation and making it fully synthesizable. Post-layout simulation results are reported using a 28 nm Bulk CMOS technology provided by TSMC. Using a 2 MHz carrier input signal and an implant depth of 1 cm, the data rate is up to 1.33 Mbit/s with a 50% duty cycle, while the minimum average power consumption is cut-down to 3.3 μW in the typical corner.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

Investigating the Intervention Parameters of Endogenous Paired Associative Stimulation (ePAS)

Advances in our understanding of neural plasticity have prompted the emergence of neuromodulatory interventions, which modulate corticomotor excitability (CME) and hold potential for accelerating stroke recovery. Endogenous paired associative stimulation (ePAS) involves the repeated pairing of a single pulse of peripheral electrical stimulation (PES) with endogenous movement-related cortical potentials (MRCPs), which are derived from electroencephalography. However, little is known about the optimal parameters for its delivery. A factorial design with repeated measures delivered four different versions of ePAS, in which PES intensities and movement type were manipulated. Linear mixed models were employed to assess interaction effects between PES intensity (suprathreshold (Hi) and motor threshold (Lo)) and movement type (Voluntary and Imagined) on CME. ePAS interventions significantly increased CME compared to control interventions, except in the case of Lo-Voluntary ePAS. There was an overall main effect for the Hi-Voluntary ePAS intervention immediately post-intervention (p = 0.002), with a sub-additive interaction effect at 30 min’ post-intervention (p = 0.042). Hi-Imagined and Lo-Imagined ePAS significantly increased CME for 30 min post-intervention (p = 0.038 and p = 0.043 respectively). The effects of the two PES intensities were not significantly different. CME was significantly greater after performing imagined movements, compared to voluntary movements, with motor threshold PES (Lo) 15 min post-intervention (p = 0.012). This study supports previous research investigating Lo-Imagined ePAS and extends those findings by illustrating that ePAS interventions that deliver suprathreshold intensities during voluntary or imagined movements (Hi-Voluntary and Hi-Imagined) also increase CME. Importantly, our findings indicate that stimulation intensity and movement type interact in ePAS interventions. Factorial designs are an efficient way to explore the effects of manipulating the parameters of neuromodulatory interventions. Further research is required to ensure that these parameters are appropriately refined to maximise intervention efficacy for people with stroke and to support translation into clinical practice.

Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography

Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient’s home.

Classification of error-related potentials from single-trial EEG in association with executed and imagined movements: a feature and classifier investigation

Error-related potentials (ErrPs) have been proposed for designing adaptive brain-computer interfaces (BCIs). Therefore, ErrPs must be decoded. The aim of this study was to evaluate ErrP decoding using combinations of different feature types and classifiers in BCI paradigms involving motor execution (ME) and imagination (MI). Fifteen healthy subjects performed 510 (ME) and 390 (MI) trials of right/left wrist extensions and foot dorsiflexions. Sham BCI feedback was delivered with an accuracy of 80% (ME) and 70% (MI). Continuous EEG was recorded and divided into ErrP and NonErrP epochs. Temporal, spectral, and discrete wavelet transform (DWT) marginals and template matching features were extracted, and all combinations of feature types were classified using linear discriminant analysis, support vector machine, and random forest classifiers. ErrPs were elicited for both ME and MI paradigms, and the average classification accuracies were significantly higher than the chance level. The highest average classification accuracy was obtained using temporal features and a combination of temporal + DWT features classified with random forest; 89 ± 9% and 83 ± 9% for ME and MI, respectively. These results generally indicate that temporal features should be used when detecting ErrPs, but there is great inter-subject variability, which means that user-specific features should be derived to maximize the performance. Graphical abstract.

EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces

Brain-computer interfaces (BCIs) can be used in neurorehabilitation; however, the literature about transferring the technology to rehabilitation clinics is limited. A key component of a BCI is the headset, for which several options are available. The aim of this study was to test four commercially available headsets' ability to record and classify movement intentions (movement-related cortical potentials-MRCPs). Twelve healthy participants performed 100 movements, while continuous EEG was recorded from the headsets on two different days to establish the reliability of the measures: classification accuracies of single-trials, number of rejected epochs, and signal-to-noise ratio. MRCPs could be recorded with the headsets covering the motor cortex, and they obtained the best classification accuracies (73%-77%). The reliability was moderate to good for the best headset (a gel-based headset covering the motor cortex). The results demonstrate that, among the evaluated headsets, reliable recordings of MRCPs require channels located close to the motor cortex and potentially a gel-based headset.