Characterization of kinesthetic motor imagery compared with visual motor imageries

Early sleep after action observation plus motor imagery improves gait and balance abilities in older adults

Action observation plus motor imagery (AOMI) is a rehabilitative approach to improve gait and balance performance. However, limited benefits have been reported in older adults. Early sleep after motor practice represents a strategy to enhance the consolidation of trained skills. Here, we investigated the effects of AOMI followed by early sleep on gait and balance performance in older adults. Forty-five older adults (mean age: 70.4 ± 5.2 years) were randomized into three groups performing a 3-week training. Specifically, AOMI-sleep and AOMI-control groups underwent observation and motor imagery of gait and balance tasks between 8:00 and 10:00 p.m. or between 8:00 and 10:00 a.m. respectively, whereas Control group observed landscape video-clips. Participants were assessed for gait performance, static and dynamic balance and fear of falling before and after training and at 1-month follow-up. The results revealed that early sleep after AOMI training sessions improved gait and balance abilities in older adults compared to AOMI-control and Control groups. Furthermore, these benefits were retained at 1-month after the training end. These findings suggested that early sleep after AOMI may represent a safe and easy-applicable intervention to minimize the functional decay in older adults.

Spatial Visual Imagery (SVI)-Based Electroencephalograph Discrimination for Natural CAD Manipulation

With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain-computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation.

Kinesthetic vs. visual focus: No evidence for effects of practice modality in representation types after action imagery practice and action execution practice

Action-imagery practice (AIP) is assumed to result in partly different action representations than action-execution practice (AEP). The present study investigated whether focusing on either kinesthetic or visual aspects of a task during practice amplifies or diminishes such differences between AIP and AEP. In ten sessions, four groups, using either AIP or AEP with either kinesthetic or visual focus, practiced a twelve-element sequence in a unimanual serial reaction time task. Tests involved the practice sequence, a mirror sequence, and a different sequence, each performed with the practice and transfer hand. In AIP and AEP, in both hands, reaction times (RTs) were shorter in the practice sequence than in the different sequence, indicating effector-independent visual-spatial sequence representations. Further, RTs were shorter in the practice hand than in the transfer hand in the practice sequence (but not in the different sequence), indicating effector-dependent representations in AEP and AIP. Although the representation types did not differ, learning effects were stronger in AEP than in AIP. Thus, although to a lower extent than in AEP, effector-dependent representations can be acquired using AIP. Contrary to the expectations, the focus manipulation did not have an impact on the acquired representation types. Hence, modality instructions in AIP may not have such a strong impact as commonly assumed, at least in implicit sequence learning.

Brief Visual Deprivation Effects on Brain Oscillations During Kinesthetic and Visual-motor Imagery

It is widely recognized that opening and closing the eyes can direct attention to external or internal stimuli processing. This has been confirmed by studies showing the effects of changes in visual stimulation changes on cerebral activity during different tasks, e.g., motor imagery and execution. However, an essential aspect of creating a mental representation of motion, such as imagery perspective, has not yet been investigated in the present context. Our study aimed to verify the effect of brief visual deprivation (under eyes open [EO] and eyes closed [EC] conditions) on brain wave oscillations and behavioral performance during kinesthetic imagery (KMI) and visual-motor imagery (VMI) tasks. We focused on the alpha and beta rhythms from visual- and motor-related EEG activity sources. Additionally, we used machine learning algorithms to establish whether the registered differences in brain oscillations might affect motor imagery brain-computer interface (MI-BCI) performance. The results showed that the occipital areas in the EC condition presented significantly stronger desynchronization during VMI tasks, which is typical for enhanced visual stimuli processing. Furthermore, the stronger desynchronization of alpha rhythms from motor areas in the EO, than EC condition confirmed previous effects obtained during real movements. It was also found that simulating movement under EC/EO conditions affected signal classification accuracy, which has practical implications for MI-BCI effectiveness. These findings suggest that shifting processing toward external or internal stimuli modulates brain rhythm oscillations associated with different perspectives on the mental representation of movement.

What networks in the brain system sustain imagination?

The brain cannot stop elaborating information. While the circuitries implied in processing sensory information, and those involved in programming and producing movements, have been extensively studied and characterized, what circuits elicit and sustain the endogenous activity (which might be referred to as imaginative activity) has not been clarified to a similar extent. The two areas which have been investigated most intensely are visual and motor imagery. Visual imagery mostly involves the same areas as visual processing and has been studied by having the subject face specific visual imagery tasks that are related to the use of the visual sketchpad as a component of the working memory system. Much less is known about spontaneous, free visual imagination, what circuits drive it, how and why. Motor imagery has been studied with several approaches: the neural circuits activated in the brain during performance of a movement have been compared with those involved in visually or kinaesthetically imagining performing the same movement, or in observing another person performing it. Some networks are similarly activated in these situations, although primary motor neurons are only activated during motor execution. Imagining the execution of an action seems unable to activate circuits involved in eliciting accompanying motor adjustments (such as postural adaptations) that are unconsciously (implicitly) associated to the execution of the movement. A more faithful neuronal activation is obtained through kinaesthetic motor imagination-imagining how it feels to perform the movement. Activation of sensory-motor and mirror systems, elicited by observing another person performing a transitive action, can also recruit circuits that sustain implicit motor responses that normally accompany the overt movement. This last aspect has originated the expanding and promising field of action observation therapy (AOT). The fact that the various kinds of motor imagery differentially involve the various brain networks may offer some hints on what neural networks sustain imagery in general, another activity that has an attentive component-recalling a memory, covertly rehearsing a speech, internally replaying a behaviour-and a vague, implicit component that arises from the freely flowing surfacing of internal images, not driven by intentional, conscious control.

Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task

Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.

Visual and kinesthetic motor imagery in adults with different degrees of self-reported motor coordination difficulties

Developmental Coordination Disorder (DCD) involves difficulties in performing coordinated movements with fine and/or gross motor skills deficits. Several studies showed that DCD is characterized by motor imagery deficits as well. Here we investigated in neurotypical adults (N = 334) the relationships between the ease of imaging two main motor imagery components, that is the visual and the kinesthetic one, self-reported motor coordination difficulties and handwriting speed. Self-reported motor difficulties were measured by the Adult Developmental Co-ordination Disorders/Dyspraxia Checklist (ADC) and scores were used to distinguish three groups: participants at risk of DCD (with both relevant childhood and current motor coordination difficulties); with motor coordination difficulties (relevant current but not childhood difficulties); without motor coordination difficulties (neither current nor childhood difficulties). The main results showed more kinesthetic and visual imagery difficulties in participants at risk of DCD than in those both with and without motor coordination difficulties. Interestingly, the relationships between the two imagery components and motor difficulties were different in the three groups, depending on: 1) the developmental phase (childhood or adulthood) to which motor coordination difficulties referred, and 2) the point of view (self or other), from which images were judged. Instead, no relationship was found between imagery abilities and handwriting speed. Thus, a nuanced pattern of the ease of imaging motor imagery emerged in adults with different degrees of self-reported motor coordination difficulties. These findings could be relevant for the assessment of people candidate to undergo a motor imagery training.

Validation of the Slovenian Version of the Movement Imagery Questionnaire for Children (MIQ-C): A Measurement Tool to Assess the Imagery Ability of Motor Tasks in Children

Purpose

The ability to perform motor imagery has been shown to influence individual athletic performance and rehabilitation. Recent evidence supports its potential as a training tool to improve motor skills in children. Although there is a standardized assessment of the imagery abilities in Slovenian-speaking adults, there is currently no validated instrument for use with Slovenian children. Therefore, the aim of the present study was to conduct a linguistic validation study of the movement imagery questionnaire for children (MIQ-C).

Methods

A total of 100 healthy children (mean age 10.3±1.3 years; 50 female) were assessed with a Slovenian version of the MIQ-C at Day 1 and Day 8. Inter-day agreement was examined using intraclass correlation coefficients (ICC). Construct validity and internal consistency were assessed using a Cronbach's alpha coefficient and exploratory - confirmatory factor analysis, respectively.

Results

The test-retest ICC were very high for all three scales examined (ICCKI=0.90; ICCIVI=0.92; ICCEVI=0.90). Excellent internal consistency (up to 0.90) was found for kinaesthetic and both visual imageries. Confirmatory analysis confirmed a three-factorial structure of the MIQ-C.

Conclusions

The Slovenian version of the MIQ-C proved to be highly reliable and valid in assessing children's motor imagery abilities, and as such for use with Slovene-speaking children. Moreover, this standardized instrument can be a helpful tool in training and rehabilitation practice with children aged 7-12 years.

Environmental enrichment through virtual reality as multisensory stimulation to mitigate the negative effects of prolonged bed rest

Prolonged bed rest causes a multitude of deleterious physiological changes in the human body that require interventions even during immobilization to prevent or minimize these negative effects. In addition to other interventions such as physical and nutritional therapy, non-physical interventions such as cognitive training, motor imagery, and action observation have demonstrated efficacy in mitigating or improving not only cognitive but also motor outcomes in bedridden patients. Recent technological advances have opened new opportunities to implement such non-physical interventions in semi- or fully-immersive environments to enable the development of bed rest countermeasures. Extended Reality (XR), which covers augmented reality (AR), mixed reality (MR), and virtual reality (VR), can enhance the training process by further engaging the kinesthetic, visual, and auditory senses. XR-based enriched environments offer a promising research avenue to investigate the effects of multisensory stimulation on motor rehabilitation and to counteract dysfunctional brain mechanisms that occur during prolonged bed rest. This review discussed the use of enriched environment applications in bedridden patients as a promising tool to improve patient rehabilitation outcomes and suggested their integration into existing treatment protocols to improve patient care. Finally, the neurobiological mechanisms associated with the positive cognitive and motor effects of an enriched environment are highlighted.

A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces

Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (~3 mm) and temporal resolution (~1 ms) among the non-invasive methods. Therefore, the MEG is an excellent modality for investigating brain mechanisms. However, publicly available MEG data remains scarce due to expensive MEG equipment, requiring a magnetically shielded room, and high maintenance costs for the helium gas supply. In this study, we share the 306-channel MEG and 3-axis accelerometer signals acquired during three-dimensional reaching movements. Additionally, we provide analysis results and MATLAB codes for time-frequency analysis, F-value time-frequency analysis, and topography analysis. These shared MEG datasets offer valuable resources for investigating brain activities or evaluating the accuracy of prediction algorithms. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements.

EEG motor imagery classification using deep learning approaches in naïve BCI users

Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the use. To reduce the effects of lack of experience in the use of BCI systems (naïve users), this paper presents the implementation of three Deep Learning (DL) methods with the hypothesis that the performance of BCI systems could be improved compared with baseline methods in the evaluation of naïve BCI users. The methods proposed here are based on Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)/Bidirectional Long Short-Term Memory (BiLSTM), and a combination of CNN and LSTM used for upper limb MI signal discrimination on a dataset of 25 naïve BCI users. The results were compared with three widely used baseline methods based on the Common Spatial Pattern (CSP), Filter Bank Common Spatial Pattern (FBCSP), and Filter Bank Common Spatial-Spectral Pattern (FBCSSP), in different temporal window configurations. As results, the LSTM-BiLSTM-based approach presented the best performance, according to the evaluation metrics of Accuracy, F-score, Recall, Specificity, Precision, and ITR, with a mean performance of 80% (maximum 95%) and ITR of 10 bits/min using a temporal window of 1.5 s. The DL Methods represent a significant increase of 32% compared with the baseline methods (p< 0.05). Thus, with the outcomes of this study, it is expected to increase the controllability, usability, and reliability of the use of robotic devices in naïve BCI users.

Neuropsychological Activations and Networks While Performing Visual and Kinesthetic Motor Imagery

This study aimed to answer the questions 'What are the neural networks and mechanisms involved in visual and kinesthetic motor imagery?', and 'Is part of cognitive processing included during visual and kinesthetic motor imagery?' by investigating the neurophysiological networks and activations during visual and kinesthetic motor imagery using motor imagery tasks (golf putting). The experiment was conducted with 19 healthy adults. Functional magnetic resonance imaging (fMRI) was used to examine neural activations and networks during visual and kinesthetic motor imagery using golf putting tasks. The findings of the analysis on cerebral activation patterns based on the two distinct types of motor imagery indicate that the posterior lobe, occipital lobe, and limbic lobe exhibited activation, and the right hemisphere was activated during the process of visual motor imagery. The activation of the temporal lobe and the parietal lobe were observed during the process of kinesthetic motor imagery. This study revealed that visual motor imagery elicited stronger activation in the right frontal lobe, whereas kinesthetic motor imagery resulted in greater activation in the left frontal lobe. It seems that kinesthetic motor imagery activates the primary somatosensory cortex (BA 2), the secondary somatosensory cortex (BA 5 and 7), and the temporal lobe areas and induces human sensibility. The present investigation evinced that the neural network and the regions of the brain that are activated exhibit variability contingent on the category of motor imagery.

Enhanced lower-limb motor imagery by kinesthetic illusion

Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system.

Review of public motor imagery and execution datasets in brain-computer interfaces

The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject- or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset's quality and recording settings as well as the use of using public datasets for future work on BCIs.

EEG changes during passive movements improve the motor imagery feature extraction in BCIs-based sensory feedback calibration

Objective.This work proposes a method for two calibration schemes based on sensory feedback to extract reliable motor imagery (MI) features, and provide classification outputs more correlated to the user's intention.Method.After filtering the raw electroencephalogram (EEG), a two-step method for spatial feature extraction by using the Riemannian covariance matrices (RCM) method and common spatial patterns is proposed here. It uses EEG data from trials providing feedback, in an intermediate step composed of bothkth nearest neighbors and probability analyses, to find periods of time in which the user probably performed well the MI task without feedback. These periods are then used to extract features with better separability, and train a classifier for MI recognition. For evaluation, an in-house dataset with eight healthy volunteers and two post-stroke patients that performed lower-limb MI, and consequently received passive movements as feedback was used. Other popular public EEG datasets (such as BCI Competition IV dataset IIb, among others) from healthy subjects that executed upper-and lower-limbs MI tasks under continuous visual sensory feedback were further used.Results.The proposed system based on the Riemannian geometry method in two-steps (RCM-RCM) outperformed significantly baseline methods, reaching average accuracy up to 82.29%. These findings show that EEG data on periods providing passive movement can be used to contribute greatly during MI feature extraction.Significance.Unconscious brain responses elicited over the sensorimotor areas may be avoided or greatly reduced by applying our approach in MI-based brain-computer interfaces (BCIs). Therefore, BCI's outputs more correlated to the user's intention can be obtained.

Oscillatory neurofeedback networks and poststroke rehabilitative potential in severely impaired stroke patients

Motor restoration after severe stroke is often limited. However, some of the severely impaired stroke patients may still have a rehabilitative potential. Biomarkers that identify these patients are sparse. Eighteen severely impaired chronic stroke patients with a lack of volitional finger extension participated in an EEG study. During sixty-six trials of kinesthetic motor imagery, a brain-machine interface turned event-related beta-band desynchronization of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. A subgroup of eight patients participated in a subsequent four-week rehabilitation training. Changes of the movement extent were captured with sensors which objectively quantified even discrete improvements of wrist movement. Albeit with the same motor impairment level, patients could be differentiated into two groups, i.e., with and without task-related increase of bilateral cortico-cortical phase synchronization between frontal/premotor and parietal areas. This fronto-parietal integration (FPI) was associated with a significantly higher volitional beta modulation range in the ipsilesional sensorimotor cortex. Following the four-week training, patients with FPI showed significantly higher improvement in wrist movement than those without FPI. Moreover, only the former group improved significantly in the upper extremity Fugl-Meyer-Assessment score. Neurofeedback-related long-range oscillatory coherence may differentiate severely impaired stroke patients with regard to their rehabilitative potential, a finding that needs to be confirmed in larger patient cohorts.

Motor imagery evokes strengthened activation in sensorimotor areas and its effective connectivity related to cognitive regions in patients with complete spinal cord injury

The objective of this study was to investigate the alterations of brain activation and effective connectivity during motor imagery (MI) in complete spinal cord injury (CSCI) patients and to reveal a potential mechanism of MI in motor rehabilitation of CSCI patients. Fifteen CSCI patients and twenty healthy controls underwent the MI task-related fMRI scan, and the motor execution (ME) task only for healthy controls. The brain activation patterns of the two groups during MI, and CSCI patients during the MI task and healthy controls during the ME task were compared. Then the significantly changed brain activation areas in CSCI patients during the MI task were used as regions of interest for effective connectivity analysis, using a voxel-wise granger causality analysis (GCA) method. Compared with healthy controls, increased activations in left primary sensorimotor cortex and bilateral cerebellar lobules IV-VI were detected in CSCI patients during the MI task, and the activation level of these areas even equaled that of healthy controls during the ME task. Furthermore, GCA revealed decreased effective connectivity from sensorimotor related areas (primary sensorimotor cortex and cerebellar lobules IV-VI) to cognitive related areas (prefrontal cortex, precuneus, middle temporal gyrus, and inferior temporal gyrus) in CSCI patients. Our findings demonstrated that motor related brain areas can be functionally preserved and activated through MI after CSCI, it maybe the potential mechanism of MI in the motor rehabilitation of CSCI patients. In addition, Sensorimotor related brain regions have less influence on the cognitive related regions in CSCI patients during MI (The trial registration number: ChiCTR2000032793).

Effects of Mirror Neurons-Based Rehabilitation Techniques in Hand Injuries: A Systematic Review and Meta-Analysis

Background: Hand trauma requires specific rehabilitation protocol depending on the different structures involved. According to type of surgical intervention, and for monitoring pain and edema, post-operative rehabilitation of a hand that has experienced trauma involves different timings for immobilization. Several protocols have been used to reduce immobilization time, and various techniques and methods are adopted, depending on the structures involved. Objective: To measure the effects of mirror neurons-based rehabilitation techniques in hand injuries throughout a systematic review and meta-analysis. Methods: The protocol was accepted in PROSPERO database. A literature search was conducted in Cinahl, Scopus, Medline, PEDro, OTseeker. Two authors independently identified eligible studies, based on predefined inclusion criteria, and extracted the data. RCT quality was assessed using the JADAD scale. Results: Seventy-nine suitable studies were screened, and only eleven were included for qualitative synthesis, while four studies were selected for quantitative analysis. Four studies were case reports/series, and seven were RCTs. Nine investigate the effect of Mirror Therapy and two the effect of Motor Imagery. Quantitative analyses revealed Mirror Therapy as effective for hand function recovery (mean difference = −14.80 95% Confidence Interval (CI) = −17.22, −12.38) (p < 0.00001) in the short term, as well as in long follow-up groups (mean difference = −13.11 95% Confidence Interval (CI) = −17.53, −8.69) (p < 0.00001). Clinical, but not statistical, efficacy was found for manual dexterity (p = 0.15), while no benefit was reported for range of motion. Conclusions: Mirror neurons-based rehabilitation techniques, combined with conventional occupational and physical therapy, can be a useful approach in hand trauma. Mirror therapy seems to be effective for hand function recovery, but, for motor imagery and action observation, there is not sufficient evidence to recommend its use. Further research on the efficacy of the mirror neurons-based technique in hand injury is recommended.

Neural oscillations during motor imagery of complex gait: an HdEEG study

The aim of this study was to investigate differences between usual and complex gait motor imagery (MI) task in healthy subjects using high-density electroencephalography (hdEEG) with a MI protocol. We characterized the spatial distribution of α- and β-bands oscillations extracted from hdEEG signals recorded during MI of usual walking (UW) and walking by avoiding an obstacle (Dual-Task, DT). We applied a source localization algorithm to brain regions selected from a large cortical-subcortical network, and then we analyzed α and β bands Event-Related Desynchronizations (ERDs). Nineteen healthy subjects visually imagined walking on a path with (DT) and without (UW) obstacles. Results showed in both gait MI tasks, α- and β-band ERDs in a large cortical-subcortical network encompassing mostly frontal and parietal regions. In most of the regions, we found α- and β-band ERDs in the DT compared with the UW condition. Finally, in the β band, significant correlations emerged between ERDs and scores in imagery ability tests. Overall we detected MI gait-related α- and β-band oscillations in cortical and subcortical areas and significant differences between UW and DT MI conditions. A better understanding of gait neural correlates may lead to a better knowledge of pathophysiology of gait disturbances in neurological diseases.

A comparison of directed functional connectivity among fist-related brain activities during movement imagery, movement execution, and movement observation

Brain-computer interface (BCI) has been widely used in sports training and rehabilitation training. It is primarily based on action simulation, including movement imagery (MI) and movement observation (MO). However, the development of BCI technology is limited due to the challenge of getting an in-depth understanding of brain networks involved in MI, MO, and movement execution (ME). To better understand the brain activity changes and the communications across various brain regions under MO, ME, and MI, this study conducted the fist experiment under MO, ME, and MI. We recorded 64-channel electroencephalography (EEG) from 39 healthy subjects (25 males, 14 females, all right-handed) during fist tasks, obtained intensities and locations of sources using EEG source imaging (ESI), computed source activation modes, and finally investigated the brain networks using spectral Granger causality (GC). The brain regions involved in the three motor conditions are similar, but the degree of participation of each brain region and the network connections among the brain regions are different. MO, ME, and MI did not recruit shared brain connectivity networks. In addition, both source activation modes and brain network connectivity had lateralization advantages.

Differential Influence of the Dorsal Premotor and Primary Somatosensory Cortex on Corticospinal Excitability during Kinesthetic and Visual Motor Imagery: A Low-Frequency Repetitive Transcranial Magnetic Stimulation Study

Consistent evidence suggests that motor imagery involves the activation of several sensorimotor areas also involved during action execution, including the dorsal premotor cortex (dPMC) and the primary somatosensory cortex (S1). However, it is still unclear whether their involvement is specific for either kinesthetic or visual imagery or whether they contribute to motor activation for both modalities. Although sensorial experience during motor imagery is often multimodal, identifying the modality exerting greater facilitation of the motor system may allow optimizing the functional outcomes of rehabilitation interventions. In a sample of healthy adults, we combined 1 Hz repetitive transcranial magnetic stimulation (rTMS) to suppress neural activity of the dPMC, S1, and primary motor cortex (M1) with single-pulse TMS over M1 for measuring cortico-spinal excitability (CSE) during kinesthetic and visual motor imagery of finger movements as compared to static imagery conditions. We found that rTMS over both dPMC and S1, but not over M1, modulates the muscle-specific facilitation of CSE during kinesthetic but not during visual motor imagery. Furthermore, dPMC rTMS suppressed the facilitation of CSE, whereas S1 rTMS boosted it. The results highlight the differential pattern of cortico-cortical connectivity within the sensorimotor system during the mental simulation of the kinesthetic and visual consequences of actions.

Effect of a Brain-Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity

Recently, studies on cycling-based brain–computer interfaces (BCIs) have been standing out due to their potential for lower-limb recovery. In this scenario, the behaviors of the sensory motor rhythms and the brain connectivity present themselves as sources of information that can contribute to interpreting the cortical effect of these technologies. This study aims to analyze how sensory motor rhythms and cortical connectivity behave when volunteers command reactive motor imagery (MI) BCI that provides passive pedaling feedback. We studied 8 healthy subjects who performed pedaling MI to command an electroencephalography (EEG)-based BCI with a motorized pedal to receive passive movements as feedback. The EEG data were analyzed under the following four conditions: resting, MI calibration, MI online, and receiving passive pedaling (on-line phase). Most subjects produced, over the foot area, significant event-related desynchronization (ERD) patterns around Cz when performing MI and receiving passive pedaling. The sharpest decrease was found for the low beta band. The connectivity results revealed an exchange of information between the supplementary motor area (SMA) and parietal regions during MI and passive pedaling. Our findings point to the primary motor cortex activation for most participants and the connectivity between SMA and parietal regions during pedaling MI and passive pedaling.