Motor Imagery and Its Practical Application

Deciphering Functional Connectivity Differences Between Motor Imagery and Execution of Target-Oriented Grasping

This study aimed to delineate overlapping and distinctive functional connectivity in visual motor imagery, kinesthetic motor imagery, and motor execution of target-oriented grasping action of the right hand. Functional magnetic resonance imaging data were obtained from 18 right-handed healthy individuals during each condition. Seed-based connectivity and multi-voxel pattern analyses were employed after selecting seed regions with the left primary motor cortex and supplementary motor area. There was equivalent seed-based connectivity during the three conditions in the bilateral frontoparietal and temporal areas. When the seed region was the left primary motor cortex, increased connectivity was observed in the left cuneus and superior frontal area during visual and kinesthetic motor imageries, respectively, compared with that during motor execution. Multi-voxel pattern analyses revealed that each condition was differentiated by spatially distributed connectivity patterns of the left primary motor cortex within the right cerebellum VI, cerebellum crus II, and left lingual area. When the seed region was the left supplementary motor area, the connectivity patterns within the right putamen, thalamus, cerebellar areas IV-V, and left superior parietal lobule were significantly classified above chance level across the three conditions. The present findings improve our understanding of the spatial representation of functional connectivity and its specific patterns among motor imagery and motor execution. The strength and fine-grained connectivity patterns of the brain areas can discriminate between motor imagery and motor execution.

Action observation and motor imagery have no effect on balance and freezing of gait in Parkinson's disease: a randomized controlled trial

BACKGROUND

Combining action observation (AO) and motor imagery (MI) training may induce greater brain activity in areas usually involved in Parkinson's disease (PD) and lead to greater behavioral and neurophysiological effects than when used separately.

AIM

To determine the effects of combining AO, MI, and gait training on balance and freezing of gait in individuals with PD.

DESIGN

This is a single-blinded, randomized controlled clinical trial.

SETTING

Laboratory of Intervention and Analysis of Movement (LIAM) from the Department of Physical Therapy of a Brazilian University.

POPULATION

Study sample consisted of individuals diagnosed with idiopathic PD by a neurologist specialized in movement disorders.

METHODS

39 individuals with PD were divided into experimental (EG=21) and control groups (CG=18). EG performed 12 sessions of AO, MI, and gait training, whereas CG watched PD-related educational videos and performed 12 sessions of gait training. Balance (measured using the Mini Balance Evaluation Systems Test [MiniBESTest]) and freezing of gait (measured using the Freezing of Gait Questionnaire) were reassessed one day after the end of the intervention.

RESULTS

We did not observe significant intra- and intergroup differences in freezing of gait. For the EG, we observed a significant intragroup difference in the total score of MiniBESTest (F=5.2; P=0.02), and sensory orientation (F=4.5; P=0.04) and dynamic gait (F=3.6; P=0.03) domains. MiniBESTest domains were not different between groups.

CONCLUSIONS

Combining AO, MI, and gait training was not more effective than isolated gait training for balance and freezing of gait in individuals with PD.

CLINICAL REHABILITATION IMPACT

MI training can moderate AO effects and enhance motor learning when both therapies are combined. Therefore, this approach may still have the potential to be included in the treatment of PD. New studies should investigate whether the factors that influence these results are related to the protocol's sensitivity in changing the evaluated parameters or to the time and intensity of AO and MI training.

On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery

Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm which requires powerful classifiers. Recent development of deep learning technology has prompted considerable interest in using deep learning for classification and resulted in multiple models. Finding the best performing models among them would be beneficial for designing better BCI systems and classifiers going forward. However, it is difficult to directly compare performance of various models through the original publications, since the datasets used to test the models are different from each other, too small, or even not publicly available. In this work, we selected five MI-EEG deep classification models proposed recently: EEGNet, Shallow & Deep ConvNet, MB3D and ParaAtt, and tested them on two large, publicly available, databases with 42 and 62 human subjects. Our results show that the models performed similarly on one dataset while EEGNet performed the best on the second with a relatively small training cost using the parameters that we evaluated.

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Over the past few years, the processing of motor imagery (MI) electroencephalography (EEG) signals has been attracted for developing brain-computer interface (BCI) applications, since feature extraction and classification of these signals are extremely difficult due to the inherent complexity and tendency to artifact properties of them. The BCI systems can provide a direct interaction pathway/channel between the brain and a peripheral device, hence the MI EEG-based BCI systems seem crucial to control external devices for patients suffering from motor disabilities. The current study presents a semi-supervised model based on three-stage feature extraction and machine learning algorithms for MI EEG signal classification in order to improve the classification accuracy with smaller number of deep features for distinguishing right- and left-hand MI tasks. Stockwell transform is employed at the first phase of the proposed feature extraction method to generate two-dimensional time–frequency maps (TFMs) from one-dimensional EEG signals. Next, the convolutional neural network (CNN) is applied to find deep feature sets from TFMs. Then, the semi-supervised discriminant analysis (SDA) is utilized to minimize the number of descriptors. Finally, the performance of five classifiers, including support vector machine, discriminant analysis, k-nearest neighbor, decision tree, random forest, and the fusion of them are compared. The hyperparameters of SDA and mentioned classifiers are optimized by Bayesian optimization to maximize the accuracy. The presented model is validated using BCI competition II dataset III and BCI competition IV dataset 2b. The performance metrics of the proposed method indicate its efficiency for classifying MI EEG signals.

BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors

Brain–computer interface (BCI) remains an emerging tool that seeks to improve the patient interaction with the therapeutic mechanisms and to generate neuroplasticity progressively through neuromotor abilities. Motor imagery (MI) analysis is the most used paradigm based on the motor cortex’s electrical activity to detect movement intention. It has been shown that motor imagery mental practice with movement-associated stimuli may offer an effective strategy to facilitate motor recovery in brain injury patients. In this sense, this study aims to present the BCI associated with visual and haptic stimuli to facilitate MI generation and control the T-FLEX ankle exoskeleton. To achieve this, five post-stroke patients (55–63 years) were subjected to three different strategies using T-FLEX: stationary therapy (ST) without motor imagination, motor imagination with visual stimulation (MIV), and motor imagination with visual-haptic inducement (MIVH). The quantitative characterization of both BCI stimuli strategies was made through the motor imagery accuracy rate, the electroencephalographic (EEG) analysis during the MI active periods, the statistical analysis, and a subjective patient’s perception. The preliminary results demonstrated the viability of the BCI-controlled ankle exoskeleton system with the beta rebound, in terms of patient’s performance during MI active periods and satisfaction outcomes. Accuracy differences employing haptic stimulus were detected with an average of 68% compared with the 50.7% over only visual stimulus. However, the power spectral density (PSD) did not present changes in prominent activation of the MI band but presented significant variations in terms of laterality. In this way, visual and haptic stimuli improved the subject’s MI accuracy but did not generate differential brain activity over the affected hemisphere. Hence, long-term sessions with a more extensive sample and a more robust algorithm should be carried out to evaluate the impact of the proposed system on neuronal and motor evolution after stroke.

Towards New Diagnostic Approaches in Disorders of Consciousness: A Proof of Concept Study on the Promising Use of Imagery Visuomotor Task

Background: advanced paraclinical approaches using functional neuroimaging and electroencephalography (EEG) allow identifying patients who are covertly aware despite being diagnosed as unresponsive wakefulness syndrome (UWS). Bedside detection of covert awareness employing motor imagery tasks (MI), which is a universally accepted clinical indicator of awareness in the absence of overt behavior, may miss some of these patients, as they could still have a certain level of awareness. We aimed at assessing covert awareness in patients with UWS using a visuomotor-guided motor imagery task (VMI) during EEG recording. Methods: nine patients in a minimally conscious state (MCS), 11 patients in a UWS, and 15 healthy individuals (control group—CG) were provided with an VMI (imagine dancing while watching a group dance video to command), a simple-MI (imagine squeezing their right hand to command), and an advanced-MI (imagine dancing without watching a group dance video to command) to detect command-following. We analyzed the command-specific EEG responses (event-related synchronization/desynchronization—ERS/ERD) of each patient, assessing whether these responses were appropriate, consistent, and statistically similar to those elicited in the CG, as reliable markers of motor imagery. Results: All patients in MCS, all healthy individuals and one patient in UWS repeatedly and reliably generated appropriate EEG responses to distinct commands of motor imagery with a classification accuracy of 60–80%. Conclusions: VMI outperformed significantly MI tasks. Therefore, patients in UWS may be still misdiagnosed despite a rigorous clinical assessment and an appropriate MI assessment. It is thus possible to suggest that motor imagery tasks should be delivered to patients with chronic disorders of consciousness in visuomotor-aided modality (also in the rehabilitation setting) to greatly entrain patient’s participation. In this regard, the EEG approach we described has the clear advantage of being cheap, portable, widely available, and objective. It may be thus considered as, at least, a screening tool to identify the patients who deserve further, advanced paraclinical approaches.

Target-oriented motor imagery for grasping action: different characteristics of brain activation between kinesthetic and visual imagery

Motor imagery (MI) for target-oriented movements, which is a basis for functional activities of daily living, can be more appropriate than non-target-oriented MI as tasks to promote motor recovery or brain-computer interface (BCI) applications. This study aimed to explore different characteristics of brain activation among target-oriented kinesthetic imagery (KI) and visual imagery (VI) in the first-person (VI-1) and third-person (VI-3) perspectives. Eighteen healthy volunteers were evaluated for MI ability, trained for the three types of target-oriented MIs, and scanned using 3 T functional magnetic resonance imaging (fMRI) under MI and perceptual control conditions, presented in a block design. Post-experimental questionnaires were administered after fMRI. Common brain regions activated during the three types of MI were the left premotor area and inferior parietal lobule, irrespective of the MI modalities or perspectives. Contrast analyses showed significantly increased brain activation only in the contrast of KI versus VI-1 and KI versus VI-3 for considerably extensive brain regions, including the supplementary motor area and insula. Neural activity in the orbitofrontal cortex and cerebellum during VI-1 and KI was significantly correlated with MI ability measured by mental chronometry and a self-reported questionnaire, respectively. These results can provide a basis in developing MI-based protocols for neurorehabilitation to improve motor recovery and BCI training in severely paralyzed individuals.

[Post-stroke rehabilitation training with a brain-computer interface: a clinical and neuropsychological study]

AIM

To evaluate the clinical efficacy of BCI-supported mental practice and to reveal specific cognitive impairment which determine mental practice ineffectiveness and inability to perform MI.

MATERIAL AND METHODS

Fifty-five hemiplegic patients after first-time stroke (median age 54. 0 [44.0; 61.0], time from onset 6.0 [3.0; 13.0] month) were randomized into two groups - BCI and sham-controlled. Severity of arm paresis was measured by Fugl-Meyer Assessment of Motor Recovery after Stroke (FMA) and Action Research Arm Test (ARAT). Twelve patients from the BCI group were examined using neuropsychological testing. After assessment, patients were trained to imagine kinesthetically a movement under control of BCI with the feedback presented via an exoskeleton. Patients underwent 12 training sessions lasting up to 30 min. In the end of the study, the scores on movement scales, electroencephalographic results obtained during training sessions were analyzed and compared to the results of neuropsychological testing.

RESULTS

Evaluation of the UL clinical assessments indicated that both groups improved on ARAT and FMA (sections A-D, H, I) but only the BCI group showed an improvement in the ARAT's grasp score (p=0.012), pinch score (p=0.012), gross movement score (p=0,002). The significant correlation was revealed between particular neuropsychological tests (Taylor Figure test, choice reaction test, Head test) and online accuracy rate.

CONCLUSION

These results suggest that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Neuropsychological testing can be used for screening before mental practice admission and promote personalized rehabilitation.

Effect of fine and gross motor training or motor imagery, delivered via novel or routine modes, on cognitive function

There is extensive literature linking motor activity to cognitive effects at various stages in life, promoting both development and the reduction of aging associated pathologies. It is unclear whether the benefits of this activity on the cognitive level are associated with brain functions that are necessary for their performance or recurrence of activity or type of activity itself. The aim of this study was to evaluate whether the type of motor activity (fine, gross, and motor imagery) in two modes (novel and routine) can affect cognitive functions such as attention, executive functions, and praxis in college students. A 2 × 3 factorial design with repeated measures was used without a control group and pre- and post-training evaluation. Fifty-three young people (14 men and 39 women) participated, with mean age of 18.94 years (SD = 1.61 years) and were divided into six groups. Each of the groups performed relevant training 20 minutes per day for five days depending on the group. Measures were taken pre and post-training for attention tests, attention span, working memory, visual constructive skills, procedural memory, and motor skills. The results show a "learning effect" from the exposure to the tests in measurements after training. It was also found that between groups, there is a difference in some of the variables of procedural memory (number of errors) and working memory. More extensive training could better reflect the effects of the training, and longitudinal evaluation could show the rate of change of functions. The main clinical implication could be the evaluation of training programs for recovery and motor training in cerebral plasticity having effect on the cognitive aspects.

Motor imagery, performance and motor rehabilitation

Motor imagery has been central to adzvances in sport performance and rehabilitation. Neuroscience has provided techniques for measurement which have aided our understanding, conceptualization and theorizing. Challenges remain in the appropriate measurement of motor imagery. Motor imagery continues to provide an impetus for new findings relating to our emotional network, embodied cognition, inhibitory processes and action representation. New directions are proposed which include exploring the physical setting and conditions in which imagery occurs and investigating if short term impairments to the motor system detract from motor imagery ability and the potential application of motor imagery for recovery.

Investigating motor imagery tasks by their neural effects - A case study

Motor imagery, one of the first investigated neural process for Brain-Computer Interfaces (BCIs) still provides a great challenge nowadays. Aiming a better and more accurate control, multiple researches have been conducted by the scientific community. Nevertheless, there is still no robust and confident application developed. In order to augment the potential referring to motor imagery, and to attract user's interest, we propose multiple motor imagery tasks in combination with different visual or auditory stimuli. We use multi-class classification for discrimination and we observe confident classification performance for the task related to user's background.