The effect of combining action observation in virtual reality with kinesthetic motor imagery on cortical activity

Enhancing the quality of kinesthetic motor imagery for complex motor skills through simulated muscle activation color visualization: Evidence from time-frequency and functional connectivity analyses

It is well established that providing visual guidance within demonstration models positively influences the quality of kinesthetic motor imagery (kMI) for complex motor skills. Given that action execution and kMI share several underlying mechanisms, we hypothesize that color-coded visual cues indicating muscle activation in demonstration models can enhance the quality of kMI in the acquisition of complex motor skills. To test this hypothesis. We employed AnyBody Modeling System to develop demonstration model videos of complex motor skills. Thirty participants (mean age = 20.3 ± 0.6 years; 7 men and 8 women per group) were assigned to an experimental group, which engaged in kMI after viewing demonstration videos supplemented with simulated muscle activation color cues, or to a control group, which performed kMI following videos without such cues. All participants scored above 5 on the Motor Imagery Questionnaire-2 (MIQ-2). The vividness of kMI was assessed using the Vividness of Motor Imagery Questionnaire-2 (VMIQ-2). A 64-channel EEG cap was utilized for data acquisition. Changes in alpha and beta range oscillations during kMI were examined, and region of interest (ROI) analysis was conducted to extract the correlation coefficient matrix among kMI-related subcortical nuclei. Our results demonstrated that the vividness of kMI in the experimental group was significantly higher than that in the control group by 19.9 % (P < 0.05). Conversely, alpha event-related synchronization (ERS) in the parietal and occipital regions, as well as ERS in the frontal, central, and temporal regions, were significantly lower in the experimental group compared to the control group. The source-functional connectivity results revealed that the primary differences between the experimental and control groups were concentrated between the left V1 and right V1, as well as among the posterior parietal cortex (PPC), dorsolateral prefrontal cortex (DLPFC), and primary motor cortex (M1). In conclusion, the demonstration model, which incorporates simulated muscle activation and color visualization, enhances the vividness of kMI in complex motor skills. This enhancement is associated with the selective inhibition of the frontal, central, and temporal brain regions, the activation of the occipital and parietal regions within brain rhythmic activity, and increased information flow between the occipital-parietal and frontal-parietal brain regions.

Impact of an upper limb motion-driven virtual rehabilitation system on residual motor function in patients with complete spinal cord injury： a pilot study

BACKGROUND

Assessing residual motor function in motor complete spinal cord injury (SCI) patients using surface electromyography (sEMG) is clinically important. Due to the prolonged loss of motor control and peripheral sensory input, patients may struggle to effectively activate residual motor function during sEMG assessments. The study proposes using virtual reality (VR) technology to enhance embodiment, motor imagery (MI), and memory, aiming to improve the activation of residual motor function and increase the sensitivity of sEMG assessments.

METHODS

By Recruiting a sample of 12 patients with AIS A/B and capturing surface electromyographic signals before, druing and after VR training, RESULTS: Most patients showed significant electromyographic improvements in activation frequency and or 5-rank frequency during or after VR training. However, one patient with severe lower limb neuropathic pain did not exhibit volitional electromyographic activation, though their pain diminished during the VR training.

CONCLUSIONS

VR can enhance the activation of patients' residual motor function by improving body awareness and MI, thereby increasing the sensitivity of sEMG assessments.

Leveraging deep learning for robust EEG analysis in mental health monitoring

Introduction

Mental health monitoring utilizing EEG analysis has garnered notable interest due to the non-invasive characteristics and rich temporal information encoded in EEG signals, which are indicative of cognitive and emotional conditions. Conventional methods for EEG-based mental health evaluation often depend on manually crafted features or basic machine learning approaches, like support vector classifiers or superficial neural networks. Despite the potential of these approaches, they often fall short in capturing the intricate spatiotemporal relationships within EEG data, leading to lower classification accuracy and poor adaptability across various populations and mental health scenarios.

Methods

To overcome these limitations, we introduce the EEG Mind-Transformer, an innovative deep learning architecture composed of a Dynamic Temporal Graph Attention Mechanism (DT-GAM), a Hierarchical Graph Representation and Analysis (HGRA) module, and a Spatial-Temporal Fusion Module (STFM). The DT-GAM is designed to dynamically extract temporal dependencies within EEG data, while the HGRA models the brain's hierarchical structure to capture both localized and global interactions among different brain regions. The STFM synthesizes spatial and temporal elements, generating a comprehensive representation of EEG signals.

Results and discussion

Our empirical results confirm that the EEG Mind-Transformer significantly surpasses conventional approaches, achieving an accuracy of 92.5%, a recall of 91.3%, an F1-score of 90.8%, and an AUC of 94.2% across several datasets. These findings underline the model's robustness and its generalizability to diverse mental health conditions. Moreover, the EEG Mind-Transformer not only pushes the boundaries of state-of-the-art EEG-based mental health monitoring but also offers meaningful insights into the underlying brain functions associated with mental disorders, solidifying its value for both research and clinical settings.

Neural signatures of motor imagery for a supernumerary thumb in VR: an EEG analysis

Human movement augmentation is a rising field of research. A promising control strategy for augmented effectors involves utilizing electroencephalography through motor imagery (MI) functions. However, performing MI of a supernumerary effector is challenging, to which MI training is one potential solution. In this study, we investigate the validity of a virtual reality (VR) environment as a medium for eliciting MI neural activations for a supernumerary thumb. Specifically, we assess whether it is possible to induce a distinct neural signature for MI of a supernumerary thumb in VR. Twenty participants underwent a two-fold experiment in which they observed movements of natural and supernumerary thumbs, then engaged in MI of the observed movements. Spectral power and event related desynchronization (ERD) analyses at the group level showed that the MI signature associated with the supernumerary thumb was indeed distinct, significantly different from both the baseline and the MI signature associated with the natural thumb, while single-trial classification showed that it is distinguishable with a 78% and 69% classification accuracy, respectively. Furthermore, spectral power and ERD analyses at the group level showed that the MI signatures associated with directional movement of the supernumerary thumb, flexion and extension, were also significantly different, and single-trial classification demonstrated that these movements could be distinguished with 60% accuracy. Fine-tuning the models further increased the respective classification accuracies, indicating the potential presence of personalized features across subjects.

Motor imagery of finger movements: Effects on cortical and muscle activities

PURPOSE

The purpose of the current study was to explore the immediate effect of motor imagery (MI) involving finger movement of a given limb on cortical response and muscle activity in healthy subjects.

METHODS

Twenty healthy right-handed adults (7 females and 13 males) with a mean + SD age of 22.05 + 6.08 years participated in the study. The beta-band event-related desynchronization (ERD) at the sensorimotor cortex and muscle activity during finger movement tasks using either the index, middle, or thumb digits on the non-dominant left hand were compared before and after an MI training session. Subjects underwent a pre-MI, MI training, and finally a post-MI session where they either performed or imagined performing a button-pushing action 50 times per session with each of the three digits.

RESULTS

The ERD power in the beta frequency band was lower in pre-MI compared to post-MI and was significantly different between the pre- and post-MI sessions for both the index and middle fingers, but not the thumb. A significant decrease was seen in the mean muscle activity during post-MI compared to pre-MI for all the digits except the thumb.

CONCLUSIONS

The results from the current study suggest that complex MI can result in motor learning and improvement in motor performance, thereby requiring less effort during motion.

Cortical activation and BCI performance during brief tactile imagery: A comparative study with motor imagery

Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30 ± 3.91 % and MI achieving 81.10 ± 2.96 %, with no significant difference between the two (p = 0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.

Variation in brain connectivity during motor imagery and motor execution in stroke patients based on electroencephalography

Objective

The objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, aiming to explore suitable forms of treatment and potential therapeutic targets.

Methods

Twenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded. Correlations between functional brain areas during MI and ME were compared.

Results

PMA and MA were significantly and positively correlated during hand MI in all participants. The power spectral density (PSD) values of PMA EEG signals were greater than those of MA during MI and ME in both groups. The functional connectivity correlation was higher in the stroke group than in healthy people during MI, especially during left-handed MI. During ME, functional connectivity correlation in the brain was more enhanced during synergy movements than during isolated movements. The regions with abnormal functional connectivity were in the 18th lead of the left PMA area.

Conclusion

Left-handed MI may be crucial in MI therapy, and the 18th lead may serve as a target for non-invasive neuromodulation to promote further recovery of limb function in patients with stroke. This may provide support for the EEG theory of neuromodulation therapy for hemiplegic patients.

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.

Community mobility and participation assessment of manual wheelchair users: a review of current techniques and challenges

According to the World Health Organization, hundreds of individuals commence wheelchair use daily, often due to an injury such as spinal cord injury or through a condition such as a stroke. However, manual wheelchair users typically experience reductions in individual community mobility and participation. In this review, articles from 2017 to 2023 were reviewed to identify means of measuring community mobility and participation of manual wheelchair users, factors that can impact these aspects, and current rehabilitation techniques for improving them. The selected articles document current best practices utilizing self-surveys, in-clinic assessments, and remote tracking through GPS and accelerometer data, which rehabilitation specialists can apply to track their patients' community mobility and participation accurately. Furthermore, rehabilitation methods such as wheelchair training programs, brain-computer interface triggered functional electric stimulation therapy, and community-based rehabilitation programs show potential to improve the community mobility and participation of manual wheelchair users. Recommendations were made to highlight potential avenues for future research.

Feasibility and usability of a virtual-reality-based sensorimotor activation apparatus for carpal tunnel syndrome patients

PURPOSE

This study aimed to assess the usability of a virtual reality-assisted sensorimotor activation (VRSMA) apparatus for individual digit rehabilitation. The study had two main objectives: Firstly, to collect preliminary data on the expectations and preferences of patients with carpal tunnel syndrome (CTS) regarding virtual reality (VR) and an apparatus-assisted therapy for their affected digits. Secondly, to evaluate the usability of the VRSMA apparatus that was developed.

METHODS

The VRSMA system consists of an apparatus that provides sensory and motor stimulation via a vibratory motor and pressure sensor attached to a button, and a virtual reality-based visual cue provided by texts overlaid on top of a 3D model of a hand. The study involved 10 CTS patients who completed five blocks of VRSMA with their affected hand, with each block corresponding to the five digits. The patients were asked to complete a user expectations questionnaire before experiencing the VRSMA, and a user evaluation questionnaire after completing the VRSMA. Expectations for VRSMA were obtained from the questionnaire results using a House of Quality (HoQ) analysis.

RESULTS

In the survey for expectations, participants rated certain attributes as important for a rehabilitation device for CTS, with mean ratings above 4 for attributes such as ease of use, ease of understanding, motivation, and improvement of hand function based on clinical evidence. The level of immersion and an interesting rehabilitation regime received lower ratings, with mean ratings above 3.5. The survey evaluating VRSMA showed that the current prototype was overall satisfactory with a mean rating of 3.9 out of 5. Based on the HoQ matrix, the highest priority for development of the VRSMA was to enhance device comfort and usage time. This was followed by the need to perform more clinical studies to provide evidence of the efficacy of the VRSMA. Other technical characteristics, such as VRSMA content and device reliability, had lower priority scores.

CONCLUSION

The current study presents a potential for an individual digit sensorimotor rehabilitation device that is well-liked by CTS patients.

Real-Time Classification of Motor Imagery Using Dynamic Window-Level Granger Causality Analysis of fMRI Data

This article presents a method for extracting neural signal features to identify the imagination of left- and right-hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery (MI) and the effective connections between these regions of interest (ROIs) were calculated using Dynamic Window-level Granger Causality (DWGC). Then, a real-time fMRI (rt-fMRI) classification system for left- and right-hand MI is developed using the Open-NFT platform. We conducted data acquisition and processing on three subjects, and all of whom were recruited from a local college. As a result, the maximum accuracy of using Support Vector Machine (SVM) classifier on real-time three-class classification (rest, left hand, and right hand) with effective connections is 69.3%. And it is 3% higher than that of traditional multivoxel pattern classification analysis on average. Moreover, it significantly improves classification accuracy during the initial stage of MI tasks while reducing the latency effects in real-time decoding. The study suggests that the effective connections obtained through the DWGC method serve as valuable features for real-time decoding of MI using fMRI. Moreover, they exhibit higher sensitivity to changes in brain states. This research offers theoretical support and technical guidance for extracting neural signal features in the context of fMRI-based studies.

Inter-task transfer of force gains is facilitated by motor imagery

Introduction

There is compelling evidence that motor imagery (MI) contributes to improve muscle strength. While strong effects have been observed for finger muscles, only few experiments with moderate benefits were conducted within applied settings targeting large upper or lower limb muscles. The aim of the present study was therefore to extend the investigation of embedded MI practice designed to improve maximal voluntary strength on a multi-joint dynamic exercise involving the lower limbs. Additionally, we tested whether targeting the content of MI on another movement than that physically performed and involving the same body parts might promote inter-task transfer of strength gains.

Methods

A total of 75 participants were randomly assigned into three groups who underwent a physical training on back squat. During inter-trial recovery periods, a first MI group (n = 25) mentally rehearsed the back squat, while a second MI group (n = 25) performed MI of a different movement involving the lower limbs (deadlift). Participants from the control group (n = 25) completed a neutral cognitive task during equivalent time. Strength and power gains were assessed ecologically using a velocity transducer device at 4 different time periods.

Results

Data first revealed that participants who engaged in MI of the back squat improved their back squat performance (p < 0.03 and p < 0.01, respectively), more than the control group (p < 0.05), hence supporting the positive effects of MI on strength. Data further supported the inter-task transfer of strength gains when MI targeted a movement that was not physically trained (p = 0.05).

Discussion

These findings provide experimental support for the use of MI during physical training sessions to improve and transfer force development.

The challenge of measuring physiological parameters during motor imagery engagement in patients after a stroke

Introduction

It is suggested that eye movement recordings could be used as an objective evaluation method of motor imagery (MI) engagement. Our investigation aimed to evaluate MI engagement in patients after stroke (PaS) compared with physical execution (PE) of a clinically relevant unilateral upper limb movement task of the patients' affected body side.

Methods

In total, 21 PaS fulfilled the MI ability evaluation [Kinaesthetic and Visual Imagery Questionnaire (KVIQ-10), body rotation task (BRT), and mental chronometry task (MC)]. During the experiment, PaS moved a cup to distinct fields while wearing smart eyeglasses (SE) with electrooculography electrodes integrated into the nose pads and electrodes for conventional electrooculography (EOG). To verify MI engagement, heart rate (HR) and oxygen saturation (SpO2) were recorded, simultaneously with electroencephalography (EEG). Eye movements were recorded during MI, PE, and rest in two measurement sessions to compare the SE performance between conditions and SE's psychometric properties.

Results

MI and PE correlation of SE signals varied between r = 0.12 and r = 0.76. Validity (cross-correlation with EOG signals) was calculated for MI (r = 0.53) and PE (r = 0.57). The SE showed moderate test-retest reliability (intraclass correlation coefficient) with r = 0.51 (95% CI 0.26-0.80) for MI and with r = 0.53 (95% CI 0.29 - 0.76) for PE. Event-related desynchronization and event-related synchronization changes of EEG showed a large variability. HR and SpO2 recordings showed similar values during MI and PE. The linear mixed model to examine HR and SpO2 between conditions (MI, PE, rest) revealed a significant difference in HR between rest and MI, and between rest and PE but not for SpO2. A Pearson correlation between MI ability assessments (KVIQ, BRT, MC) and physiological parameters showed no association between MI ability and HR and SpO2.

Conclusion

The objective assessment of MI engagement in PaS remains challenging in clinical settings. However, HR was confirmed as a reliable parameter to assess MI engagement in PaS. Eye movements measured with the SE during MI did not resemble those during PE, which is presumably due to the demanding task. A re-evaluation with task adaptation is suggested.

Investigating the influence of functional electrical stimulation on motor imagery related μ-rhythm suppression

Background

Motor Imagery (MI) is a well-known cognitive technique that utilizes the same neural circuits as voluntary movements. Therefore, MI practice is widely used in sport training and post-stroke rehabilitation. The suppression of the μ-rhythm in electroencephalogram (EEG) is a conventional marker of sensorimotor cortical activation during motor imagery. However, the role of somatosensory afferentation in mental imagery processes is not yet clear. In this study, we investigated the impact of functional electrical stimulation (FES) on μ-rhythm suppression during motor imagery.

Methods

Thirteen healthy experienced participants were asked to imagine their right hand grasping, while a 30-channel EEG was recorded. FES was used to influence sensorimotor activation during motor imagery of the same hand.

Results

We evaluated cortical activation by estimating the μ-rhythm suppression index, which was assessed in three experimental conditions: MI, MI + FES, and FES. Our findings shows that motor imagery enhanced by FES leads to a more prominent μ-rhythm suppression. Obtained results suggest a direct effect of peripheral electrical stimulation on cortical activation, especially when combined with motor imagery.

Conclusion

This research sheds light on the potential benefits of integrating FES into motor imagery-based interventions to enhance cortical activation and holds promise for applications in neurorehabilitation.