Combining Movement-Related Cortical Potentials and Event-Related Desynchronization to Study Movement Preparation and Execution

Mu-ERD reflects action understanding, but the effect is small

Since the mid-2000's, many researchers have provided evidence that mu-ERD measured at the motor cortex may reflect the collective activation of upstream brain regions associated with the human mirror system during action observation paradigms; however, several recent papers have called these findings into question. Our study represents an effort to address these criticisms. In our study, participants watched videos in which the type of grip an actor used to grasp a coffee mug either conveyed the goal with 100 % certainty (unambiguous-goal trials), or offered no predictive information (ambiguous-goal trials). If mu-ERD indexes action understanding, then we predicted that mu-ERD should increase while participants watched the actor grasp the mug for unambiguous-goal trials, but not for ambiguous-goal trials. During the intervals where participants watched the actor execute the goal, mu-ERD for unambiguous-goal trials should remain steady, whereas mu-ERD for ambiguous-goal trials should now increase. Conversely, if mu-ERD does not index action understanding, and instead reflects general motor processes associated with action (such as the activation of population vectors in M1 or planning processes), then mu-ERD should show no difference across conditions. Across most comparisons, we found that mu-ERD mostly reflected general motor processes; however, there was a small effect when participants overserved unambiguous-goal trials while watching the actor execute the goal suggesting that mu-ERD does reflect mirroring, but the effect is small.

Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities

Electroencephalogram (EEG) signals are critical in interpreting sensorimotor activities for predicting body movements. However, their efficacy in identifying intralimb movements, such as the dorsiflexion and plantar flexion of the foot, remains suboptimal. This study aims to explore whether various EEG signal quantities can effectively recognize intralimb movements to facilitate the development of Brain-Computer Interface (BCI) devices for foot rehabilitation. This research involved twenty-two healthy, right-handed participants. EEG data were collected using 21 electrodes positioned over the motor cortex, while two electromyography (EMG) electrodes recorded the onset of ankle joint movements. The study focused on analyzing slow cortical potential (SCP) and sensorimotor rhythms (SMR) in alpha and beta bands from the EEG. Five key features-fourth-order Autoregressive feature, variance, waveform length, standard deviation, and permutation entropy-were extracted. A modified Recurrent Neural Network (RNN) including Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms was developed for movement recognition. These were compared against conventional machine learning algorithms, including nonlinear Support Vector Machine (SVM) and k Nearest Neighbourhood (kNN) classifiers. The performance of the proposed models was assessed using two data schemes: within-subject and across-subjects. The findings demonstrated that the GRU and LSTM models significantly outperformed traditional machine learning algorithms in recognizing different EEG signal quantities for intralimb movement. The study indicates that deep learning models, particularly GRU and LSTM, hold superior potential over standard machine learning techniques in identifying intralimb movements using EEG signals. Where the accuracies of LSTM for within and across subjects were 98.87 ± 1.80 % and 87.38 ± 0.86 % respectively. Whereas the accuracy of GRU within and across subjects were 99.18 ± 1.28 % and 86.44 ± 0.69 % respectively. This advancement could significantly benefit the development of BCI devices aimed at foot rehabilitation, suggesting a new avenue for enhancing physical therapy outcomes.

Libet's legacy: A primer to the neuroscience of volition

The neuroscience of volition is an emerging subfield of the brain sciences, with hundreds of papers on the role of consciousness in action formation published each year. This makes the state-of-the-art in the discipline poorly accessible to newcomers and difficult to follow even for experts in the field. Here we provide a comprehensive summary of research in this field since its inception that will be useful to both groups. We also discuss important ideas that have received little coverage in the literature so far. We systematically reviewed a set of 2220 publications, with detailed consideration of almost 500 of the most relevant papers. We provide a thorough introduction to the seminal work of Benjamin Libet from the 1960s to 1980s. We also discuss common criticisms of Libet's method, including temporal introspection, the interpretation of the assumed physiological correlates of volition, and various conceptual issues. We conclude with recent advances and potential future directions in the field, highlighting modern methodological approaches to volition, as well as important recent findings.

Brain-computer interface for robot control with eye artifacts for assistive applications

Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user's eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors' knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot.

Swallow-related Brain Activity in Post-total Laryngectomy Patients: A Case Series Study

Background

Total laryngectomy is a surgical procedure to completely remove the hyoid bone, larynx, and associated muscles as a curative treatment for laryngeal cancer. This leads to insufficient swallowing function with compensative movements of the residual tongue to propel the food bolus to the pharynx and esophagus. However, the neurophysiological mechanisms of compensative swallowing after total laryngectomy remain unclear. Recently, swallowing-related cortical activation such as event-related desynchronization (ERD) during swallowing has been reported in healthy participants and neurological patients with dysphagia. Abnormal ERD elucidates the pathophysiological cortical activities that are related to swallowing. No report has investigated ERD in post-total laryngectomy patients.

Case

We investigated ERD during volitional swallowing using electroencephalography in three male patients after total laryngectomy for laryngeal cancer (age and time after surgery: Case 1, 75 years, 10 years; Case 2, 85 years, 19 years; Case 3, 73 years, 19 years). In video fluorographic swallowing studies, we observed compensatory tongue movements such as posterior-inferior retraction of the tongue and contact on the posterior pharyngeal wall in all three cases. Significant ERD was localized in the bilateral medial sensorimotor areas and the left lateral parietal area in Case 1, in the bilateral frontal and left temporal areas in Case 2, and in the left prefrontal and premotor areas in Case 3.

Discussion

These results suggest that cortical activities related to swallowing might reflect cortical reorganization for modified swallowing movements of residual tongue muscles to compensate for reduced swallowing pressure in patients after total laryngectomy.

Mirror visual feedback during unilateral finger movements is related to the desynchronization of cortical electroencephalographic somatomotor alpha rhythms

Using a mirror adequately oriented, the motion of just one hand induces the illusion of the movement with the other hand. Here, we tested the hypothesis that such a mirror phenomenon may be underpinned by an electroencephalographic (EEG) event-related desynchronization/synchronization (ERD/ERS) of central alpha rhythms (around 10 Hz) as a neurophysiological measure of the interactions among cerebral cortex, basal ganglia, and thalamus during movement preparation and execution. Eighteen healthy right-handed male participants performed standard auditory-triggered unilateral (right) or bilateral finger movements in the No Mirror (M-) conditions. In the Mirror (M+) condition, the unilateral right finger movements were performed in front of a mirror oriented to induce the illusion of simultaneous left finger movements. EEG activity was recorded from 64 scalp electrodes, and the artifact-free event-related EEG epochs were used to compute alpha ERD. In the M- conditions, a bilateral prominent central alpha ERD was observed during the bilateral movements, while left central alpha ERD and right alpha ERS were seen during unilateral right movements. In contrast, the M+ condition showed significant bilateral and widespread alpha ERD during the unilateral right movements. These results suggest that the above illusion of the left movements may be related to alpha ERD measures reflecting excitatory desynchronizing signals in right lateral premotor and primary somatomotor areas possibly in relation to basal ganglia-thalamic loops.

Influence of the number of trials on evoked motor cortical activity in EEG recordings

OBJECTIVE

Improvements in electroencephalography enable the study of the localization of active brain regions during motor tasks. Movement-related cortical potentials (MRCPs), and event-related desynchronization (ERD) and synchronization (ERS) are the main motor-related cortical phenomena/neural correlates observed when a movement is elicited. When assessing neurological diseases, averaging techniques are commonly applied to characterize motor related processes better. In this case, a large number of trials is required to obtain a motor potential that is representative enough of the subject's condition. This study aimed to assess the effect of a limited number of trials on motor-related activity corresponding to different upper limb movements (elbow flexion/extension, pronation/supination and hand open/close).

APPROACH

An open dataset consisting on 15 healthy subjects was used for the analysis. A Monte Carlo simulation approach was applied to analyse, in a robust way, different typical time- and frequency-domain features, topography, and low-resolution tomography (LORETA).

MAIN RESULTS

Grand average potentials, and topographic and tomographic maps showed few differences when using fewer trials, but shifts in the localization of motor-related activity were found for several individuals. MRCP and beta ERD features were more robust to a limited number of trials, yielding differences lower than 20% for cases with 50 trials or more. Strong correlations between features were obtained for subsets above 50 trials. However, the inter-subject variability increased as the number of trials decreased. The elbow flexion/extension movement showed a more robust performance for a limited number of trials, both in population and in individual-based analysis.

SIGNIFICANCE

Our findings suggested that 50 trials can be an appropriate number to obtain stable motor-related features in terms of differences in the averaged motor features, correlation, and changes in topography and tomography.

Dynamic corticomuscular multi-regional modulations during finger movement revealed by time-varying network analysis

OBJECTIVE

A body movement involves the complicated information exchange between the central and peripheral systems, which is characterized by the dynamical coupling patterns between the multiple brain areas and multiple muscle units. How the central and peripheral nerves coordinate multiple internal brain regions and muscle groups is very important when accomplishing the action.

APPROACH

In this study, we extend the adaptive directed transfer function to construct the time-varying networks between multiple corticomuscular regions and divide the movement duration into different stages by the time-varying corticomuscular network patterns.

MAIN RESULTS

The inter dynamical corticomuscular network demonstrated the different interaction patterns between the central and peripheral systems during the different hand movement stages. The muscles transmit bottom-up movement information in the preparation stage, but the brain issues top-down control commands and dominates in the execution stage, and finally, the brain's dominant advantage gradually weakens in the relaxation stage. When classifying the different movement stages based on time-varying corticomuscular network indicators, an average accuracy above 74% could be reliably achieved.

SIGNIFICANCE

The findings of this study help deepen our knowledge of central-peripheral nerve pathways and coordination mechanisms, and also provide opportunities for monitoring and regulating movement disorders.

Case Report: Event-Related Desynchronization Observed During Volitional Swallow by Electroencephalography Recordings in ALS Patients With Dysphagia

Dysphagia is a severe disability affecting daily life in patients with amyotrophic lateral sclerosis (ALS). It is caused by degeneration of both the bulbar motor neurons and cortical motoneurons projecting to the oropharyngeal areas. A previous report showed decreased event-related desynchronization (ERD) in the medial sensorimotor areas in ALS dysphagic patients. In the process of degeneration, brain reorganization may also be induced in other areas than the sensorimotor cortices. Furthermore, ALS patients with dysphagia often show a longer duration of swallowing. However, there have been no reports on brain activity in other cortical areas and the time course of brain activity during prolonged swallowing in these patients. In this case report, we investigated the distribution and the time course of ERD and corticomuscular coherence (CMC) in the beta (15–25 Hz) frequency band during volitional swallow using electroencephalography (EEG) in two patients with ALS. Case 1 (a 71-year-old man) was diagnosed 2 years before the evaluation. His first symptom was muscle weakness in the right hand; 5 months later, dysphagia developed and exacerbated. Since his dietary intake decreased, he was given an implantable venous access port. Case 2 (a 64-year-old woman) was diagnosed 1 year before the evaluation. Her first symptom was open-nasal voice and dysarthria; 3 months later, dysphagia developed and exacerbated. She was given a percutaneous endoscopic gastrostomy. EEG recordings were performed during volitional swallowing, and the ERD was calculated. The average swallow durations were 7.6 ± 3.0 s in Case 1 and 8.3 ± 2.9 s in Case 2. The significant ERD was localized in the prefrontal and premotor areas and lasted from a few seconds after the initiation of swallowing to the end in Case 1. The ERD was localized in the lateral sensorimotor areas only at the initiation of swallowing in Case 2. CMC was not observed in either case. These results suggest that compensatory processes for cortical motor outputs might depend on individual patients and that a new therapeutic approach using ERD should be developed according to the individuality of ALS patients with dysphagia.

EEG–EMG coupling as a hybrid method for steering detection in car driving settings

Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Event-Related Desynchronization and Corticomuscular Coherence Observed During Volitional Swallow by Electroencephalography Recordings in Humans

Swallowing in humans involves many cortical areas although it is partly mediated by a series of brainstem reflexes. Cortical motor commands are sent to muscles during swallow. Previous works using magnetoencephalography showed event-related desynchronization (ERD) during swallow and corticomuscular coherence (CMC) during tongue movements in the bilateral sensorimotor and motor-related areas. However, there have been few analogous works that use electroencephalography (EEG). We investigated the ERD and CMC in the bilateral sensorimotor, premotor, and inferior prefrontal areas during volitional swallow by EEG recordings in 18 healthy human subjects. As a result, we found a significant ERD in the beta frequency band and CMC in the theta, alpha, and beta frequency bands during swallow in those cortical areas. These results suggest that EEG can detect the desynchronized activity and oscillatory interaction between the cortex and pharyngeal muscles in the bilateral sensorimotor, premotor, and inferior prefrontal areas during volitional swallow in humans.

The time-varying networks of the wrist extension in post-stroke hemiplegic patients

Hemiplegia is a common dysfunction caused by the brain stroke and leads to movement disability. Although the lateralization of movement-related potential, the event-related desynchronization, and more complicated inter-regional information coupling have been investigated, seldom studies have focused on investigating the dynamic information exchanging among multiple brain regions during motor execution for post-stroke hemiplegic patients. With high temporal-resolution electroencephalogram (EEG), the time-varying network is able to reflect the dynamical complex network modalities corresponding to the movements at a millisecond level. In our present study, the wrist extension experiment was designed, along with related EEG datasets being collected. Thereafter, the corresponding time-varying networks underlying the wrist extension were accordingly constructed by adopting the adaptive directed transfer function and then statistically explored, to further uncover the dynamic network deficits (i.e., motor dysfunction) in post-stroke hemiplegic patients. Results of this study found the effective connectivity between the stroked motor area and other areas decreased in patients when compared to healthy controls; on the contrary, the enhanced connectivity between non-stroked motor areas and other areas, especially the frontal and parietal-occipital lobes, were further identified for patients during their accomplishing the designed wrist extension, which might dynamically compensate for the deficited patients' motor behaviors. These findings not only helped deepen our knowledge of the mechanism underlying the patients' motor behaviors, but also facilitated the real-time strategies for clinical therapy of brain stroke, as well as providing a reliable biomarker to predict the future rehabilitation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09738-2.

Prediction of isometric handgrip force from graded event-related desynchronization of the sensorimotor rhythm

Objective. Brain-computer interfaces (BCIs) show promise as a direct line of communication between the brain and the outside world that could benefit those with impaired motor function. But the commands available for BCI operation are often limited by the ability of the decoder to differentiate between the many distinct motor or cognitive tasks that can be visualized or attempted. Simple binary command signals (e.g. right hand at rest versus movement) are therefore used due to their ability to produce large observable differences in neural recordings. At the same time, frequent command switching can impose greater demands on the subject's focus and takes time to learn. Here, we attempt to decode the degree of effort in a specific movement task to produce a graded and more flexible command signal.Approach.Fourteen healthy human subjects (nine male, five female) responded to visual cues by squeezing a hand dynamometer to different levels of predetermined force, guided by continuous visual feedback, while the electroencephalogram (EEG) and grip force were monitored. Movement-related EEG features were extracted and modeled to predict exerted force.Main results.We found that event-related desynchronization (ERD) of the 8-30 Hz mu-beta sensorimotor rhythm of the EEG is separable for different degrees of motor effort. Upon four-fold cross-validation, linear classifiers were found to predict grip force from an ERD vector with mean accuracies across subjects of 53% and 55% for the dominant and non-dominant hand, respectively. ERD amplitude increased with target force but appeared to pass through a trough that hinted at non-monotonic behavior.Significance.Our results suggest that modeling and interactive feedback based on the intended level of motor effort is feasible. The observed ERD trends suggest that different mechanisms may govern intermediate versus low and high degrees of motor effort. This may have utility in rehabilitative protocols for motor impairments.

Error related EEG potentials evoked by visuo-motor rotations

Electroencephalographic (EEG) correlates of errors, known as error-related potentials (ErrPs), provide promising tools to investigate error processing in the brain and to detect and correct errors induced by brain-computer interfaces (BCIs). Visuo-motor rotation (VMR) is a well-known experimental paradigm to introduce visuo-motor errors that closely mimics directional errors induced by BCIs. However, investigations of ErrPs during VMR experiments are limited and reveals different ErrPs depending on task and synchronization. We conducted VMR experiments with 5 randomly selected conditions (no-rotation, small, ±22.5°, or large, ±45° rotations) to hamper adaptation and facilitate investigation of the effect of error size. We tracked eye movements so EEG was synchronized not only to onset of movement correction (OMC) but also to saccadic movement onset (SMO). Kinematic analysis indicated that maximum deviations from a straight line to the target were larger in trials with large rotations compared to small or no rotations, but there was a large overlap. Thus, we also compared ErrPs generated by trials with different maximum deviations. Our results reveal that trials with large rotations and especially trials with large maximum deviations evoke a significant positive ErrP component. The positive peak appeared 380 msec after SMO and 240 msec after OMC. Furthermore, the positive peak was associated with activity in Brodmann areas 5 and 7, in agreement with other studies and with the role of posterior parietal cortex in reaching movements. The observed ErrP may facilitate further investigation of error processing in the brain and error detection and correction in BCIs.

Shoulder Flexion Pre-Movement Recognition Through Subject-Specific Brain Regions to Command an Upper Limb Exoskeleton

This work presents two brain-computer interfaces (BCIs) for shoulder pre-movement recognition using: 1) manual strategy for Electroencephalography (EEG) channels selection, and 2) subject-specific channels selection by applying non-negative factorization matrix (NMF). Besides, the proposed BCIs compute spatial features extracted from filtered EEG signals through Riemannian covariance matrices and a linear discriminant analysis (LDA) to discriminate both shoulder pre-movement and rest states. We studied on twenty-one healthy subjects different frequency ranges looking the best frequency band for shoulder pre-movement recognition. As a result, our BCI located automatically EEG channels on the contralateral moved limb, and enhancing the pre-movement recognition (ACC = 71.39 ± 12.68%, κ = 0.43 ± 0.25%). The ability of the proposed BCIs to select specific EEG locations more cortically related to the moved limb could benefit the neuro-rehabilitation process.

EEG Changes in Time and Time-Frequency Domain During Movement Preparation and Execution in Stroke Patients

This study investigated electroencephalogram (EEG) changes during movement preparation and execution in stroke patients. EEG-based event-related potential (ERP) technology was used to measure brain activity changes. Seventeen stroke patients participated in this study and completed ERP tests that were designed to measure EEG changes during unilateral upper limb movements in preparation and execution stages, with Instruction Response Movement (IRM) and Cued Instruction Response Movement (CIRM) paradigms. EEG data were analyzed using motor potential (MP) in the time domain and the mu-rhythm and beta frequency band response mean value (R-means) in the time-frequency domain. In IRM, the MP amplitude at Cz was higher during hemiplegic arm movement than during unaffected arm movement. MP latency was shorter at Cz and the contralesional motor cortex during hemiplegic arm movement in CIRM compared to IRM. No significant differences were found in R-means among locations, between movement sides in both ERP tests. This study presents the brain activity changes in the time and time-frequency domains in stroke patients during movement preparation and execution and supports the contralesional compensation and adjacent-region compensation mechanism of post-stroke brain reconstruction. These findings may contribute to future rehabilitation research about neuroplasticity and technology development such as the brain-computer interface.

Decreased neural inhibitory state in fibromyalgia pain: A cross-sectional study

OBJECTIVES

Chronic pain is one of the most common and challenging symptoms in fibromyalgia (FM). Currently, self-reported pain is the main criterion used by clinicians assessing patients with pain. However, it is subjective, and multiple factors can affect pain levels. In this study, we investigated the neural correlates of FM pain using conditioned pain modulation (CPM), electroencephalography (EEG), and transcranial magnetic stimulation (TMS).

METHODS

In this cross-sectional neurophysiological analysis of a randomized, double-blind controlled trial, 36 patients with fibromyalgia were included. We analyzed CPM, EEG variables and TMS measures and their correlation with pain levels as measured by a visual analog scale. Univariate and multivariate linear regression analyses were performed to identify the predictors of pain severity.

RESULTS

We found: (1) no association between pain levels and CPM; (2) an association between reduced alpha and beta power over the central region in resting-EEG and higher pain levels; (3) an association between smaller event-related desynchronization (ERD) responses in theta and delta bands over the central region and higher pain levels; (4) an association between smaller ERD responses in theta and delta bands and smaller intracortical inhibition and higher intracortical facilitation ratios; (5) an association between smaller ERD responses in delta band and reduced CPM.

CONCLUSIONS

Our results do not support CPM as a biomarker for pain intensity in FM. However, our specific EEG findings showing the relationship between pain, CPM and TMS measures suggest that FM leads to a disruption of inhibitory neural modulators and thus support CPM as a likely predictive marker of disrupted pain modulation system. These neurophysiological markers need to be further explored in potential future trials as to find novel targets for the treatment of FM.

Decoding Movement-Related Cortical Potentials Based on Subject-Dependent and Section-Wise Spectral Filtering

An important challenge in developing a movement-related cortical potential (MRCP)-based brain-machine interface (BMI) is an accurate decoding of the user intention for real-world environments. However, the performance remains insufficient for real-time decoding owing to the endogenous signal characteristics compared to other BMI paradigms. This study aims to enhance the MRCP decoding performance from the perspective of preprocessing techniques (i.e., spectral filtering). To the best of our knowledge, existing MRCP studies have used spectral filters with a fixed frequency bandwidth for all subjects. Hence, we propose a subject-dependent and section-wise spectral filtering (SSSF) method that considers the subjects' individual MRCP characteristics for two different temporal sections. In this study, MRCP data were acquired under a powered exoskeleton environments in which the subjects conducted self-initiated walking. We evaluated our method using both our experimental data and a public dataset (BNCI Horizon 2020). The decoding performance using the SSSF was 0.86 (±0.09), and the performance on the public dataset was 0.73 (±0.06) across all subjects. The experimental results showed a statistically significant enhancement ( ) compared with the fixed frequency bands used in previous methods on both datasets. In addition, we presented successful decoding results from a pseudo-online analysis. Therefore, we demonstrated that the proposed SSSF method can involve more meaningful MRCP information than conventional methods.

Effectiveness of Upper Limb Wearable Technology for Improving Activity and Participation in Adult Stroke Survivors: Systematic Review

BACKGROUND

With advances in technology, the adoption of wearable devices has become a viable adjunct in poststroke rehabilitation. Upper limb (UL) impairment affects up to 77% of stroke survivors impacting on their ability to carry out everyday activities. However, despite an increase in research exploring these devices for UL rehabilitation, little is known of their effectiveness.

OBJECTIVE

This review aimed to assess the effectiveness of UL wearable technology for improving activity and participation in adult stroke survivors.

METHODS

Randomized controlled trials (RCTs) and randomized comparable trials of UL wearable technology for poststroke rehabilitation were included. Primary outcome measures were validated measures of activity and participation as defined by the International Classification of Functioning, Disability, and Health. Databases searched were MEDLINE, Web of Science (Core collection), CINAHL, and the Cochrane Library. The Cochrane Risk of Bias Tool was used to assess the methodological quality of the RCTs and the Downs and Black Instrument for the quality of non RCTs.

RESULTS

In the review, we included 11 studies with collectively 354 participants at baseline and 323 participants at final follow-up including control groups and participants poststroke. Participants' stroke type and severity varied. Only 1 study found significant between-group differences for systems functioning and activity (P≤.02). The 11 included studies in this review had small sample sizes ranging from 5 to 99 participants at an average (mean) age of 57 years.

CONCLUSIONS

This review has highlighted a number of reasons for insignificant findings in this area including low sample sizes and the appropriateness of the methodology for complex interventions. However, technology has the potential to measure outcomes, provide feedback, and engage users outside of clinical sessions. This could provide a platform for motivating stroke survivors to carry out more rehabilitation in the absence of a therapist, which could maximize recovery.

TRIAL REGISTRATION

PROSPERO CRD42017057715; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=57715.

Optimised transcranial direct current stimulation (tDCS) for fibromyalgia-targeting the endogenous pain control system: a randomised, double-blind, factorial clinical trial protocol

INTRODUCTION

Fibromyalgia (FM) is a common debilitating condition with limited therapeutic options. Medications have low efficacy and are often associated with adverse effects. Given that FM is associated with a defective endogenous pain control system and central sensitisation, combining interventions such as transcranial direct current stimulation (tDCS) and aerobic exercise (AE) to modulate pain-processing circuits may enhance pain control.

METHODS AND ANALYSIS

A prospective, randomised (1:1:1:1), placebo-controlled, double-blind, factorial clinical trial will test the hypothesis that optimised tDCS (16 anodal tDCS sessions combined with AE) can restore of the pain endogenous control system. Participants with FM (n=148) will undergo a conditioning exercise period and be randomly allocated to one of four groups: (1) active tDCS and AE, (2) sham tDCS and AE, (3) active tDCS and non-aerobic exercise (nAE) or (4) sham tDCS and nAE. Pain inhibitory activity will be assessed using conditioned pain modulation (CPM) and temporal slow pain summation (TSPS)-primary outcomes. Secondary outcomes will include the following assessments: Transcranial magnetic stimulation and electroencephalography as cortical markers of pain inhibitory control and thalamocortical circuits; secondary clinical outcomes on pain, FM, quality of life, sleep and depression. Finally, the relationship between the two main mechanistic targets in this study-CPM and TSPS-and changes in secondary clinical outcomes will be tested. The change in the primary efficacy endpoint, CPM and TSPS, from baseline to week 4 of stimulation will be tested with a mixed linear model and adjusted for important demographic variables.

ETHICS AND DISSEMINATION

This study obeys the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of Partners Healthcare under the protocol number 2017P002524. Informed consent will be obtained from participants. Study findings will be reported in conferences and peer-reviewed journal publications.

TRIAL REGISTRATION NUMBER

NCT03371225.