Relation Between EEG Measures and Upper Limb Motor Recovery in Stroke Patients: A Scoping Review

Post-stroke spontaneous motor recovery in mice can be predicted from acute-phase local field potential using machine learning

Stroke remains a leading cause of long-term disability, underscoring the urgent need for effective predictors of motor recovery. Understanding the electrophysiological changes underlying spontaneous recovery could offer critical insight into recovery mechanisms and aid in predicting individual rehabilitation trajectories. In this study, we investigated the predictive power of local field potentials recorded 2 days post-stroke to forecast 1 month motor recovery in a mouse model of ischemic stroke. By employing a comprehensive machine learning approach, we identified key electrophysiological features that significantly enhanced prediction accuracy. Through nested leave-one-animal-out cross-validation, we achieved high prediction accuracy, correctly identifying motor recovery status in 15 out of 16 mice. Our findings also revealed that pre-stroke brain activity did not contribute to prediction accuracy, suggesting that post-stroke dynamics are the primary determinants of recovery. Notably, we found that features from the contralesional hemisphere were particularly influential in predicting recovery outcomes, underscoring the critical role of the non-lesioned hemisphere in motor recovery. Our data-driven methodology underscores the importance of balancing feature selection to optimize predictive performance, particularly in the context of spontaneous recovery, where insight into natural recovery processes can guide the development of targeted rehabilitation strategies. Ultimately, our findings advocate for a deeper understanding of post-stroke brain dynamics to improve clinical outcomes for stroke patients.

The role of high-density EEG in diagnosis and prognosis of neurological diseases: A systematic review

OBJECTIVE

The use of High-Density Electroencephalography (HD-EEG) increased in neurological disorders, due to analysis of brain connectivity. This method is able to create a detailed brain mapping. The aim is to investigate studies that employed HD-EEG in neurological and neurodegenerative filed.

METHODS

This systematic review was conducted and reported in accordance with the PRISMA. A research terms was conducted for: (1) dementia, (2) Multiple Sclerosis (MS), (3) Parkinson Disease (PD), (4) stroke, (5) epilepsy.

RESULTS

The study included a total of 89 articles: 22 dementia; 33 epilepsy; 5 MS; 24 PD; 5 S. Articles were discussed for each neurological disorder and for different types of EEG analysis: analysis of event-related potentials, specific EEG features at resting state, spectral and connectivity analysis, time-frequency analysis and EEG recordings combined with other types of intervention.

DISCUSSION

HD-EEG recordings provide evidence about the evaluation of early markers of the disease onset, mapping of cortical activity distribution of neurological disorders.

SIGNIFICANCE

HD-EEG demonstrated it effectiveness in detection of biomarkers for the diagnosis and prognosis. In dementia contributed to misdiagnosis between different subtype and identifying markers of cognitive decline, investigating motor and cognitive networks dynamics in stroke, PD and MS, and to detect task-specific network reorganization.

Can motion capture improve task-based fMRI studies of motor function post-stroke? A systematic review

BACKGROUND

Variability in motor recovery after stroke represents a major challenge in its understanding and management. While functional MRI has been used to unravel interactions between stroke motor function and clinical outcome, fMRI alone cannot clarify any relation between brain activation and movement characteristics.

OBJECTIVES

We aimed to identify fMRI and kinematic coupling approaches and to evaluate their potential contribution to the understanding of motor function post-stroke.

METHOD

A systematic literature review was performed according to PRISMA guidelines on studies using fMRI and kinematics in post-stroke individuals. We assessed the internal, external, statistical, and technological validity of each study. Data extraction included study design and analysis procedures used to couple brain activity with movement characteristics.

RESULTS

Of the 404 studies found, 23 were included in the final review. The overall study quality was moderate (0.6/1). Thirteen studies used kinematic information either parallel to the fMRI results, or as a real-time input to external devices, for instance to provide feedback to the patient. Ten studies performed a statistical analysis between movement and brain activity by either using kinematics as variables during group or individual level regression or correlation. This permitted establishing links between movement characteristics and brain activity, unraveling cortico-kinematic relationships. For instance, increased activity in the ipsilesional Premotor Cortex was related to less smooth movements, whereas trunk compensation was expressed by increased activity in the contralesional Primary Motor Cortex.

CONCLUSION

Our review suggests that the coupling of fMRI and kinematics may provide valuable insight into cortico-kinematic relationships. The optimization and standardization of both data measurement and treatment procedures may help the field to move forward and to fully use the potential of multimodal cortico-kinematic integration to unravel the complexity of post-stroke motor function and recovery.

Neuroplasticity changes in cortical activity, grey matter, and white matter of stroke patients after upper extremity motor rehabilitation via a brain-computer interface therapy program

Objective. Upper extremity (UE) motor function loss is one of the most impactful consequences of stroke. Recently, brain-computer interface (BCI) systems have been utilized in therapy programs to enhance UE motor recovery after stroke, widely attributed to neuroplasticity mechanisms. However, the effect that the BCI's closed-loop feedback can have in these programs is unclear. The aim of this study was to quantitatively assess and compare the neuroplasticity effects elicited in stroke patients by a UE motor rehabilitation BCI therapy and by its sham-BCI counterpart.Approach. Twenty patients were randomly assigned to either the experimental group (EG), who controlled the BCI system via UE motor intention, or the control group (CG), who received random feedback. The elicited neuroplasticity effects were quantified using asymmetry metrics derived from electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) data acquired before, at the middle, and at the end of the intervention, alongside UE sensorimotor function evaluations. These asymmetry indexes compare the affected and unaffected hemispheres and are robust to lesion location variability.Main results. Most patients from the EG presented brain activity lateralisation to one brain hemisphere, as described by EEG (8 patients) and fMRI (6 patients) metrics. Conversely, the CG showed less pronounced lateralisations, presenting primarily bilateral activity patterns. DTI metrics showed increased white matter integrity in half of the EG patients' unaffected hemisphere, and in all but 2 CG patients' affected hemisphere. Individual patient analysis suggested that lesion location was relevant since functional and structural lateralisations occurred towards different hemispheres depending on stroke site.Significance. This study shows that a BCI intervention can elicit more pronounced neuroplasticity-related lateralisations than a sham-BCI therapy. These findings could serve as future biomarkers, helping to better select patients and increasing the impact that a BCI intervention can achieve. Clinical trial: NCT04724824.

Association of TMS-EEG interhemispheric imbalance with upper limb motor impairment in chronic stroke patients: An exploratory study

OBJECTIVE

We aimed to investigate the involvement of interhemispheric cortical dynamics as measured by combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) in recovery of upper limb (UL) motor functions in chronic stroke patients.

METHODS

Ten patients with a history of single ischemic chronic stroke were enrolled (4F, 63.8 ± 9.9 years). Each patient underwent TMS-EEG recordings to evaluate interhemispheric cortical dynamics as well as a reaching task recorded with inertial measurement units, and a series of clinical assessments. TMS-EEG neurophysiological data were analysed considering spatiotemporal, power response, and interhemispheric balance (IHB) dynamics.

RESULTS

We found that IHB index (IHBi) and low-frequency power (LFP) (4-13 Hz) in the affected hemisphere were associated with the degree of UL impairment.

CONCLUSION

Increased IHBi due to stroke is an unfavourable factor of UL' functions. Similarly, LFP of both hemispheres is strongly correlated with clinical and kinematic outcomes.

SIGNIFICANCE

TMS-EEG biomarkers of interhemispheric unbalance could be used to estimate functional recovery and drive tailored neuromodulation and neurorehabilitation approaches.

Resting-State Electroencephalogram Microstate and Correlations with Motor Function and Balance in Chronic Stroke

Objective: This study aimed to compare electroencephalogram microstates of patients with chronic stroke to healthy subjects and correlated microstates with clinical and functional characteristics in stroke. Methods: This cross-sectional, exploratory and correlational study was performed with chronic stroke patients (n = 27) and healthy subjects (n = 27) matched for age and gender. We recorded electroencephalography microstates using 32 channels during eyes-closed and eyes-open conditions and analyzed the four classic microstates maps (A, B, C, D). Post-stroke participants were assessed using the modified Rankin Scale and the Fugl-Meyer Scale. All participants were assessed for cognitive function, fear of falling, and static balance. Student's t-test was used to compare groups and Pearson's correlation coefficient was used to assess correlations between microstates parameters and stroke-related clinical outcomes. Results: In the eyes-open condition, moderate correlations were observed between the duration of microstate C and functional disability. In the eyes-closed condition, moderate correlations were observed between the coverage of microstate C, the occurrence of microstate C and D, and the duration of microstate B with functional aspects (eg, lower limb motor function, balance, functional disability, and fear of falling). Conclusions: Changes in microstates and correlations between topographies and clinical and functional aspects suggest that electroencephalogram could be used as a biomarker in stroke patients.

EEG Responses to Upper Limb Pinprick Stimulation in Acute and Early Subacute Motor and Sensorimotor Stroke: A Proof of Concept

Electroencephalogram (EEG) during pinprick stimulation has the potential to unveil neural mechanisms underlying sensorimotor impairments post-stroke. A proof-of-concept study explored event-related peak pinprick amplitude and oscillatory responses in healthy controls and in people with acute and subuacute motor and sensorimotor stroke, their relationship, and to what extent EEG somatosensory responses can predict sensorimotor impairment. In this study, 26 individuals participated, 10 people with an acute and early subacute sensorimotor stroke, 6 people with an acute and early subacute motor stroke, and 10 age-matched controls. Pinpricks were applied to the dorsa of the impaired hand to collect somatosensory evoked potentials. Time(-frequency) analyses of somatosensory evoked potential (SEP) data at electrodes C3 and C4 explored peak pinprick amplitude and oscillatory responses across the three groups. Also, in stroke, (sensori-)motor impairments were assessed with the Fugl Meyer Assessment Upper Extremity (FMA) and Erasmus modified Nottingham Sensory Assessment (EmNSA) at baseline and 7 to 14 days later. Mixed model analyses were used to address objectives. It was demonstrated that increased beta desynchronization magnitude correlated with milder motor impairments (R2adjusted = 0.213), whereas increased beta resynchronization and delta power were associated to milder somatosensory impairment (R2adjusted = 0.550). At the second session, larger peak-to-peak SEP amplitude and beta band resynchronization at baseline were related to greater improvements in EMNSA and FMA scores, respectively, in the sensorimotor stroke group. These findings highlight the potential of EEG combined with somatosensory stimuli to differentiate between sensorimotor and motor impairments in stroke, offering preliminary insights into both diagnostic and prognostic aspects of upper limb recovery.

Altered Resting-State Electroencephalogram Microstate Characteristics in Stroke Patients

BACKGROUND

Stroke remains a leading cause of disability globally and movement impairment is the most common complication in stroke patients. Resting-state electroencephalography (EEG) microstate analysis is a non-invasive approach of whole-brain imaging based on the spatiotemporal pattern of the entire cerebral cortex. The present study aims to investigate microstate alterations in stroke patients.

METHODS

Resting-state EEG data collected from 24 stroke patients and 19 healthy controls matched by age and gender were subjected to microstate analysis. For four classic microstates labeled as class A, B, C and D, their temporal characteristics (duration, occurrence and coverage) and transition probabilities (TP) were extracted and compared between the two groups. Furthermore, we explored their correlations with clinical outcomes including the Fugl-Meyer assessment (FMA) and the action research arm test (ARAT) scores in stroke patients. Finally, we analyzed the relationship between the temporal characteristics and spectral power in frequency bands. False discovery rate (FDR) method was applied for correction of multiple comparisons.

RESULTS

Microstate analysis revealed that the stroke group had lower occurrence of microstate A which was regarded as the sensorimotor network (SMN) compared with the control group (p = 0.003, adjusted p = 0.036, t = -2.959). The TP from microstate A to microstate D had a significant positive correlation with the Fugl-Meyer assessment of lower extremity (FMA-LE) scores (p = 0.049, r = 0.406), but this finding did not survive FDR adjustment (adjusted p = 0.432). Additionally, the occurrence and the coverage of microstate B were negatively correlated with the power of delta band in the stroke group, which did not pass adjustment (p = 0.033, adjusted p = 0.790, r = -0.436; p = 0.026, adjusted p = 0.790, r = -0.454, respectively).

CONCLUSIONS

Our results confirm the abnormal temporal dynamics of brain activity in stroke patients. The study provides further electrophysiological evidence for understanding the mechanism of brain motor functional reorganization after stroke.

The cerebellum and the Mirror Neuron System: A matter of inhibition? From neurophysiological evidence to neuromodulatory implications. A narrative review

Mirror neurons show activity during both the execution (AE) and observation of actions (AO). The Mirror Neuron System (MNS) could be involved during motor imagery (MI) as well. Extensive research suggests that the cerebellum is interconnected with the MNS and may be critically involved in its activities. We gathered evidence on the cerebellum's role in MNS functions, both theoretically and experimentally. Evidence shows that the cerebellum plays a major role during AO and MI and that its lesions impair MNS functions likely because, by modulating the activity of cortical inhibitory interneurons with mirror properties, the cerebellum may contribute to visuomotor matching, which is fundamental for shaping mirror properties. Indeed, the cerebellum may strengthen sensory-motor patterns that minimise the discrepancy between predicted and actual outcome, both during AE and AO. Furthermore, through its connections with the hippocampus, the cerebellum might be involved in internal simulations of motor programs during MI. Finally, as cerebellar neuromodulation might improve its impact on MNS activity, we explored its potential neurophysiological and neurorehabilitation implications.

Facilitating Corticomotor Excitability of the Contralesional Hemisphere Using Non-Invasive Brain Stimulation to Improve Upper Limb Motor Recovery from Stroke-A Scoping Review

Upper limb weakness following stroke poses a significant global psychosocial and economic burden. Non-invasive brain stimulation (NIBS) is a potential adjunctive treatment in rehabilitation. However, traditional approaches to rebalance interhemispheric inhibition may not be effective for all patients. The supportive role of the contralesional hemisphere in recovery of upper limb motor function has been supported by animal and clinical studies, particularly for those with severe strokes. This review aims to provide an overview of the facilitation role of the contralesional hemisphere for post-stroke motor recovery. While more studies are required to predict responses and inform the choice of NIBS approach, contralesional facilitation may offer new hope for patients in whom traditional rehabilitation and NIBS approaches have failed.

An Efficient Deep Transfer Learning Network for Characterization of Stroke Patients' Motor Execution from Multi-Channel EEG-Recordings

Recent advances in stroke rehabilitation technology have been focused on developing Intelligent Rehabilitation Robots (IRR) that can effectively engage post-stroke patients (PSP) in intuitive motor training for full function recovery. Most existing rehabilitation robots incorporate functionalities that are passive in nature, constraining PSP to predetermined trajectories that often deviate from patients' limb movement intentions, consequently hindering recovery. To resolve this issue, a robust deep-transfer learning driven network (DTLN) is developed to adequately characterize PSP's motion intention signatures from neural oscillations towards achieving intuitive and active training. Thus, we investigated and proposed the utilization of mu-frequency spectrum (muFS) based CWT approach for Scalograms construction, which serves as inputs to the DTLN model that characterizes multiple classes of PSP's motor execution signatures from multi-channel electroencephalography (EEG) recordings. Then, we evaluated the proposed method using EEG data from six PSP and compared the decoding results to those of related approaches under similar experimental settings. The proposed method resulted in a significant increment of 10.84 % - 13.19% decoding accuracy across stroke patients and better convergence in comparison to other methods. Additionally, the method exhibited distinct task separability for individual motor execution signature across patients. In conclusion, our method offers a consistently accurate decoding of motor tasks that could enable intuitively active robotic training in PSPs with impaired motor function.

Quantitative EEG and prognosis for recovery in post-stroke patients: The effect of lesion laterality

OBJECTIVE

There is emerging confidence that quantitative EEG (qEEG) has the potential to inform clinical decision-making and guide individualized rehabilitation after stroke, but consensus on the best EEG biomarkers is needed for translation to clinical practice. This study investigates the spatial qEEG spectral and symmetry distribution in patients with a left/right hemispheric stroke, to evaluate their side-specific prognostic power in post-acute rehabilitation outcome.

METHODS

Resting-state 19-channel EEG recordings were collected with clinical information on admission to intensive inpatient rehabilitation (within 30 days post stroke), and six months post stroke. After preprocessing, spectral (Delta-to-Alpha Ratio, DAR) and symmetry (pairwise and hemispheric Brain Symmetry Index) features were extracted. Patients were divided into Affected Right and Left (AR/AL) groups, according to the location of their lesion. Within each group, DAR was compared between homologous electrode pairs and the pairwise difference between pairs was compared across pairs in the scalp. Then, the prognostic power of qEEG admission metrics was evaluated by performing correlations between admission metrics and discharge mBI values.

RESULTS

Fifty-two patients with hemorrhagic or ischemic stroke (20 females, 38.5 %, median age 76 years [IQR = 22]) were included in the study. DAR was significantly higher in the affected hemisphere for both AR and AL groups, and, a higher frontal (to posterior) asymmetry was found independent of the side of the lesion. DAR was found to be a prognostic marker of 6-months modified Barthel Index (mBI) only for the AL group, while hemispheric asymmetry did not correlate with follow-up outcomes in either group.

DISCUSSION

While the presence of EEG abnormalities in the affected hemisphere of a stroke is well recognized, we have shown that the extent of DAR abnormalities seen correlates with disability at 6 months post stroke, but only for left hemispheric lesions. Routine prognostic evaluation, in addition to motor and functional scales, can add information concerning neuro-prognostication and reveal neurophysiological abnormalities to be assessed during rehabilitation.

Benefits of Robot-Assisted Upper-Limb Rehabilitation from the Subacute Stage after a Stroke of Varying Severity: A Multicenter Randomized Controlled Trial

BACKGROUND

The aim of this study was to compare the clinical effectiveness of robot-assisted therapy with that of conventional occupational therapy according to the onset and severity of stroke.

METHODS

In this multicenter randomized controlled trial, stroke patients were randomized (1:1) to receive robot-assisted therapy or conventional occupational therapy. The robot-assisted training group received 30 min of robot-assisted therapy twice and 30 min of conventional occupational therapy daily, while the conventional therapy group received 90 min of occupational therapy. Therapy was conducted 5 days/week for 4 weeks. The primary outcome was the Wolf Motor Function Test (WMFT) score after 4 and 8 weeks of therapy.

RESULTS

Overall, 113 and 115 patients received robot-assisted and conventional therapy, respectively. The WMFT score after robot-assisted therapy was not significantly better than that after conventional therapy, but there were significant improvements in the Motricity Index (trunk) and the Fugl-Meyer Assessment. After robot-assisted therapy, wrist strength significantly improved in the subacute or moderate-severity group of stroke patients.

CONCLUSIONS

Robot-assisted therapy improved the upper-limb functions and activities of daily living (ADL) performance as much as conventional occupational therapy. In particular, it showed signs of more therapeutic effectiveness in the subacute stage or moderate-severity group.

A review of combined functional neuroimaging and motion capture for motor rehabilitation

BACKGROUND

Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation.

RESEARCH OBJECTIVE

This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach.

METHODS

This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted.

RESULTS

Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships.

CONCLUSION

The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.

EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs

Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.

Rehabilitation Modulates High-Order Interactions Among Large-Scale Brain Networks in Subacute Stroke

The recovery of motor functions after stroke is fostered by the functional integration of large-scale brain networks, including the motor network (MN) and high-order cognitive controls networks, such as the default mode (DMN) and executive control (ECN) networks. In this paper, electroencephalography signals are used to investigate interactions among these three resting state networks (RSNs) in subacute stroke patients after motor rehabilitation. A novel metric, the O-information rate (OIR), is used to quantify the balance between redundancy and synergy in the complex high-order interactions among RSNs, as well as its causal decomposition to identify the direction of information flow. The paper also employs conditional spectral Granger causality to assess pairwise directed functional connectivity between RSNs. After rehabilitation, a synergy increase among these RSNs is found, especially driven by MN. From the pairwise description, a reduced directed functional connectivity towards MN is enhanced after treatment. Besides, inter-network connectivity changes are associated with motor recovery, for which the mediation role of ECN seems to play a relevant role, both from pairwise and high-order interactions perspective.

A Multimodal Analysis to Explore Upper Limb Motor Recovery at 4 Weeks After Stroke: Insights From EEG and Kinematics Measures

Background. Stroke is a leading cause of death and disability worldwide and there is a very short period of increased synaptic plasticity, fundamental in motor recovery. Thus, it is crucial to acquire data to guide the rehabilitation treatment. Promising results have been achieved with kinematics and neurophysiological data, but currently, few studies integrate these different modalities. Objectives. We explored the correlations between standardized clinical scales, kinematic data, and EEG measures 4 weeks after stroke. Methods. 26 patients were considered. Among them, 20 patients also performed the EEG study, beyond the kinematic analysis, at 4 weeks. Results. We found correlations between the Fugl-Meyer Assessment-Upper Extremity, movement duration, smoothness measures, and velocity peaks. Moreover, EEG measures showed a tendency for the healthy hemisphere to vicariate the affected one in patients characterized by better clinical conditions. Conclusions. These results suggest the relevance of kinematic (in particular movement duration and smoothness) and EEG biomarkers to evaluate post-stroke recovery. We emphasize the importance of integrating clinical data with kinematic and EEG analyses from the early stroke stages, in order to guide rehabilitation strategies to best leverage the short period of increased synaptic plasticity.

Brain network characteristics between subacute and chronic stroke survivors in active, imagery, passive movement task: a pilot study

Background

The activation patterns and functional network characteristics between stroke survivors and healthy individuals based on resting-or task-state neuroimaging and neurophysiological techniques have been extensively explored. However, the discrepancy between stroke patients at different recovery stages remains unclear.

Objective

To investigate the changes in brain connectivity and network topology between subacute and chronic patients, and hope to provide a basis for rehabilitation strategies at different stages after stroke.

Methods

Fifteen stroke survivors were assigned to the subacute group (SG, N = 9) and chronic group (CG, N = 6). They were asked to perform hand grasping under active, passive, and MI conditions when recording EEG. The Fugl-Meyer Assessment Upper Extremity subscale (FMA_UE), modified Ashworth Scale (MAS), Manual Muscle Test (MMT), grip and pinch strength, modified Barthel Index (MBI), and Berg Balance Scale (BBS) were measured.

Results

Functional connectivity analyses showed significant interactions on frontal, parietal and occipital lobes connections in each frequency band, particularly in the delta band. The coupling strength of premotor cortex, M1, S1 and several connections linked to frontal, parietal, and occipital lobes in subacute subjects were lower than in chronic subjects in low alpha, high alpha, low beta, and high beta bands. Nodal clustering coefficient (CC) analyses revealed that the CC in chronic subjects was higher than in subacute subjects in the ipsilesional S1 and occipital area, contralesional dorsolateral prefrontal cortex and parietal area. Characteristic path length (CPL) analyses showed that CPL in subacute subjects was lower than in chronic subjects in low beta, high beta, and gamma bands. There were no significant differences between subacute and chronic subjects for small-world property.

Conclusion

Subacute stroke survivors were characterized by higher transfer efficiency of the entire brain network and weak local nodal effects. Transfer efficiency was reduced, the local nodal role was strengthened, and more neural resources needed to be mobilized to perform motor tasks for chronic survivors. Overall, these results may help to understand the remodeling pattern of the brain network for different post-stroke stages on task conditions and the mechanism of spontaneous recovery.

Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor

Research advancement has spurred the usage of electroencephalography (EEG)-based neural oscillatory rhythms as a biomarker to complement clinical rehabilitation strategies for the recovery of motor functions in stroke survivors. However, the inevitable contamination of EEG signals with artifacts from various sources limits its utilization and effectiveness. Thus, the integration of Independent Component Analysis (ICA) and Independent Component Label (ICLabel) has been widely employed to separate neural activity from artifacts. A crucial step in the ICLabel preprocessing pipeline is the artifactual ICs rejection threshold (TH) parameter, which determines the overall signal's quality. For instance, selecting a high TH will cause many ICs to be rejected, thereby leading to signal over-cleaning, and choosing a low TH may result in under-cleaning of the signal. Toward determining the optimal TH parameter, this study investigates the effect of six different TH groups (NO-TH and TH1-TH6) on EEG signals recorded from post-stroke patients who performed four distinct motor imagery (MI) tasks including wrist and grasping movements. Utilizing the EEG-beta band signal at the brain's sensorimotor cortex, the performance of the TH groups was evaluated using three notable EEG quantifiers. Overall, the obtained result shows that the considered THs will significantly alter neural oscillatory patterns. Comparing the performance of the TH-groups, TH-3 with a confidence level of 60% showed consistently stronger signal desynchronization and lateralization. The correlation result shows that most of the electrode pairs with high correlation values are replicable across all the MI tasks. It also revealed that brain activity correlates linearly with distance, and a strong correlation between electrode pairs is independent of the different brain cortices. The study outcome may facilitate adequate therapeutic intervention for stroke rehab.Clinical Relevance: This study indicated that optimal selection of the ICLabel artifactual rejection threshold is essential for EEG enhancement for adequate signal characterization. Thus, a TH-values with a confidence level between 50% - 70% would be suggested for artifactual ICs rejection in MI-EEG.

Development of Low-Contact-Impedance Dry Electrodes for Electroencephalogram Signal Acquisition

Dry electroencephalogram (EEG) systems have a short set-up time and require limited skin preparation. However, they tend to require strong electrode-to-skin contact. In this study, dry EEG electrodes with low contact impedance (<150 kΩ) were fabricated by partially embedding a polyimide flexible printed circuit board (FPCB) in polydimethylsiloxane and then casting them in a sensor mold with six symmetrical legs or bumps. Silver-silver chloride paste was used at the exposed tip of each leg or bump that must touch the skin. The use of an FPCB enabled the fabricated electrodes to maintain steady impedance. Two types of dry electrodes were fabricated: flat-disk electrodes for skin with limited hair and multilegged electrodes for common use and for areas with thick hair. Impedance testing was conducted with and without a custom head cap according to the standard 10-20 electrode arrangement. The experimental results indicated that the fabricated electrodes exhibited impedance values between 65 and 120 kΩ. The brain wave patterns acquired with these electrodes were comparable to those acquired using conventional wet electrodes. The fabricated EEG electrodes passed the primary skin irritation tests based on the ISO 10993-10:2010 protocol and the cytotoxicity tests based on the ISO 10993-5:2009 protocol.

Neurophysiological and Clinical Effects of Upper Limb Robot-Assisted Rehabilitation on Motor Recovery in Patients with Subacute Stroke: A Multicenter Randomized Controlled Trial Study Protocol

BACKGROUND

The efficacy of upper limb (UL) robot-assisted therapy (RAT) on functional improvement after stroke remains unclear. However, recently published randomized controlled trials have supported its potential benefits in enhancing the activities of daily living, arm and hand function, and muscle strength. Task-specific and high-intensity exercises are key points in facilitating motor re-learning in neurorehabilitation since RAT can provide an assisted-as-needed approach. This study aims to investigate the clinical effects of an exoskeleton robotic system for UL rehabilitation compared with conventional therapy (CT) in people with subacute stroke. As a secondary aim, we seek to identify patients' characteristics, which can predict better recovery after UL-RAT and detects whether it could elicit greater brain stimulation.

METHODS

A total of 84 subacute stroke patients will be recruited from 7 Italian rehabilitation centers over 3 years. The patients will be randomly allocated to either CT (control group, CG) or CT plus UL-RT through an Armeo^®Power (Hocoma AG, CH, Volketswil, Switzerland) exoskeleton (experimental group, EG). A sample stratification based on distance since onset, DSO (DSO ≤ 30; DSO > 30), and Fugl-Meyer Assessment (FM)-UL (FM-UL ≤ 22; 22 < FM-UL ≤ 44) will be considered for the randomization. The outcomes will be recorded at baseline (T0), after 25 + 3 sessions of intervention (T1), and at 6 months post-stroke (T2). The motor functioning assessed by the FM-UL (0-66) will be considered the primary outcome. The clinical assessments will be set based on the International Classification of Function, Disability and Health (ICF). A patient satisfaction questionnaire will be evaluated in the EG at T1. A subgroup of patients will be evaluated at T0 and T1 via electroencephalography. Their brain electrical activity will be recorded during rest conditions with their eyes closed and open (5 min each).

CONCLUSION

The results of this trial will provide an in-depth understanding of the efficacy of early UL-RAT through a whole arm exoskeleton and how it may relate to the neural plasticity process. The trial was registered at ClinicalTrial.gov with the registration identifier NCT04697368.

Action Observation Therapy for Arm Recovery after Stroke: A Preliminary Investigation on a Novel Protocol with EEG Monitoring

This preliminary study introduces a novel action observation therapy (AOT) protocol associated with electroencephalographic (EEG) monitoring to be used in the future as a rehabilitation strategy for the upper limb in patients with subacute stroke. To provide initial evidence on the usefulness of this method, we compared the outcome of 11 patients who received daily AOT for three weeks with that of patients who undertook two other approaches recently investigated by our group, namely intensive conventional therapy (ICT), and robot-assisted therapy combined with functional electrical stimulation (RAT-FES). The three rehabilitative interventions showed similar arm motor recovery as indexed by Fugl-Meyer's assessment of the upper extremity (FMA_UE) and box and block test (BBT). The improvement in the FMA_UE was yet more favourable in patients with mild/moderate motor impairments who received AOT, in contrast with patients carrying similar disabilities who received the other two treatments. This suggests that AOT might be more effective in this subgroup of patients, perhaps because the integrity of their mirror neurons system (MNS) was more preserved, as indexed by EEG recording from central electrodes during action observation. In conclusion, AOT may reveal an effective rehabilitative tool in patients with subacute stroke; the EEG evaluation of MNS integrity may help to select patients who could maximally benefit from this intervention.