Motor Imagery Processes Research Articles

Boostering motor imagery processing to improve gait in patients with Parkinson disease and freezing of gait: A pilot study

BackgroundGiven that patients with Parkinson's Disease (PD) and Freezing of Gait (FoG) may lack the cognitive resources necessary to activate the motor imagery (MI) process, investigating how to boost MI vividness and accuracy could be a valuable therapeutic strategy in MI Practice (MIP). ObjectiveWe aim to evaluate the priming effect of visual, or auditory, or attentional stimuli in enhancing MI ability by using quantitative data on gait and turning performance. MethodsNineteen PD participants with FoG underwent four one-week sessions of MIP, with pre and post clinical assessments. Each session included MI alone or one of three booster MI tasks (Attentional, Action observation, or Auditory) before imagining and executing walking straight and performing a 180° turn. Gait and turning performances were evaluated using six inertial sensors before and after each session. ResultsOur findings showed that both MI and boosted MI induced similar improvement in gait (speed and stride length) and 180° (step number and velocity) and 360° turning (velocity, angle) parameters compared to baseline. When differences among “booster” tasks were analyzed, results showed that Auditory and Attentional boosted MI were superior to MI alone in some gait and turning parameters. At the end of the 4 sessions, MI ability measured by means of Kinesthetic and Visual Imagery Questionnaire and Gait Imagery Questionnaire and FoG symptoms were also improved. ConclusionOur preliminary results suggest that boosting MI is a feasible strategy for enhancing MI ability and addressing FoG symptoms. Auditory and Attentional conditions appear to enhance the priming effect of MI on gait and turning performance more effectively.

Exploring the role of disgust in hands and feet laterality judgement tasks.

The hand laterality task (HLT) and the foot laterality task (FLT) are used to explore motor imagery, the ability to imagine an action without executing it. With our limbs, we interact with our body, with others, and with the environment. These contacts might cause negative feelings, such as disgust. Disgust is elicited by different drivers. For instance, body products and body envelope violations provoke disgust to avoid contaminations and to avoid damaging our bodies. However, not much is known about how disgust changes our motor imagery processes. In this study, we examined whether there is any difference in the ability to imagine hands and feet when these are emotionally charged with reminders of disgust. Thirty-six participants completed an online version of a classic (neutral) HLT and FLT and two emotionally charged (disgust) versions. Our findings show that when body parts are modified so that they elicit emotional processing, disgust is salient overall, rather than being salient specifically for actions. This is true for both our hands and our feet.

Motor Imagery and Pain Processing in Patients With Entrapment Neuropathies: A Cross-sectional Study.

(1) To assess the ability to generate both kinesthetic and visual motor imagery in participants with carpal tunnel syndrome (CTS), compared with asymptomatic participants. (2) To assess the influence of psychophysiological and functional variables in the motor imagery process. Twenty patients with unilateral CTS and 18 pain-free individuals were recruited. An observational case-control study with a nonprobability sample was conducted to assess visual and kinesthetic movement imagery ability and psychophysiological variables in patients with CTS compared with asymptomatic participants in a control group. The trial was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology statement. CTS patients have more difficulties in generating visual motor images compared with asymptomatic individuals ( t =-2.099; P <0.05; d=0.70). They need more time to complete the mental tasks (visual t =-2.424; P <0.05 and kinesthetic t =-2.200; P <0.05). A negative correlation was found between the ability to imagine and functional deficits ( r =-0.569; P =0.021) for the kinesthetic subscale and temporal summation ( r =-0.515; P <0.5). A positive correlation was found between pain pressure threshold homolateral (homolateral) and time to generate the visual mental images ( r =0.537; P <0.05). CTS patients have greater difficulty generating motor images than asymptomatic individuals. Patients also spend more time during mental tasks. CTS patients present a relationship between temporal summation and the capacity to generate kinesthetic images. In addition, the CST patients presented a correlation between chronometry mental tasking and mechanical hyperalgesia.

Differences in motor imagery abilities in active and sedentary individuals: new insights from backward-walking imagination

Evidence has shown that imagining a complex action, like backward-walking, helps improve the execution of the gesture. Despite this, studies in sport psychology have provided heterogeneous results on the use of motor imagery (MI) to improve performance. We aimed to fill this gap by analyzing how sport experience influences backward-walking MI processes in a sample of young women (n = 41, mean age = 21 ± 2.2) divided into Active and Sedentary. All participants were allocated to two randomized mental chronometric tasks, in which they had first to imagine and then execute forward-walking (FW) and backward-walking (BW). The Isochrony Efficiency measured the difference between imagination and execution times in both conditions (FW and BW). Moreover, we analyzed the ability to vividly imagine FW and BW within various perspectives in both groups through the Vividness of Movement Imagery Questionnaire (VMIQ-2). Findings showed that active individuals performed better in the BW imagery task when compared to sedentary ones (F1,39 = 4.98; p = 0.03*), while there were no differences between groups in the FW imagery task (F1,39 = .10; p = 0.75). Further, VMIQ-2 had evidenced that the ability to imagine backward is influenced by perspective used. Specifically, the use of internal visual imagery (IVI) led to worse Isochrony Efficiency (t32,25 = 2.16; p = 0.04*), while the use of kinesthetic imagery (KIN) led to better Isochrony Efficiency (t32,25 = − 2.34; p = 0.03*). These results suggest a close relation between motor experience and complex motor imagery processes and open new insights for studying these mental processes.

AN EMD AND IMF ENERGY ENTROPY-BASED OPTIMIZED FEATURE EXTRACTION AND CLASSIFICATION SCHEME FOR SINGLE TRIAL EEG SIGNAL

For the purpose of improving the classification accuracy of single-trial electroencephalogram (EEG) signal during motor imagery (MI) process, this study proposed a classification method which combines intrinsic mode functions (IMFs) energy entropy and improved empirical mode decomposition (EMD) scheme. Singular value decomposition (SVD), Gaussian mixture model (GMM), EMD and IMF energy entropy were employed for the newly designed scheme. After removing noise and artifacts from acquired EEG signals in EEGLAB, SVD was applied, and the singular values were clustered by GMM. The insignificant characteristics indicated by the small SVD values were then removed, and the signals were reconstructed, feeding to EMD algorithm. Those IMFs mapping to [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] frequencies were selected as the major features of the Electroencephalogram signal. The SVM classifier with Radial Basis Function Neural Network, linear, and polynomial kernel functions and voting mechanism then kicked in for classification. The results were compared with that of the traditional EMD and EEMD through simulation, showing that the proposed scheme can eliminate mode mixing effectively and improve the single-trial EEG signal classification accuracy significantly, suggesting the probability of designing a more efficient EEG control system based on the proposed scheme.

Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance.

The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input consists of spatial covariance matrices derived from EEGs. In order to synthesize these spatial covariance matrices and facilitate future improvements of geometric deep learning classifiers, we propose a generative modeling technique based on state-of-the-art score-based models. The quality of generated samples is evaluated through visual and quantitative assessments using a left/right-hand-movement motor imagery dataset. The exceptional pixel-level resolution of these generative samples highlights the formidable capacity of score-based generative modeling. Additionally, the center (Fréchet mean) of the generated samples aligns with neurophysiological evidence that event-related desynchronization and synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency bands during motor imagery processing. The quantitative evaluation revealed that 84.3% of the generated samples could be accurately predicted by a pre-trained classifier and an improvement of up to 8.7% in the average accuracy over ten runs for a specific test subject in a holdout experiment.

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.

Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data.

BackgroundThe aging of the world population poses a major health challenge, and brain–computer interface (BCI) technology has the potential to provide assistance and rehabilitation for the elderly.ObjectivesThis study aimed to investigate the electroencephalogram (EEG) characteristics during motor imagery by comparing young and elderly, and study Convolutional Neural Networks (CNNs) classification for the elderly population in terms of fatigue analysis in both frontal and parietal regions.MethodsA total of 20 healthy individuals participated in the study, including 10 young and 10 older adults. All participants completed the left- and right-hand motor imagery experiment. The energy changes in the motor imagery process were analyzed using time–frequency graphs and quantified event-related desynchronization (ERD) values. The fatigue level of the motor imagery was assessed by two indicators: (θ + α)/β and θ/β, and fatigue-sensitive channels were distinguished from the parietal region of the brain. Then, rhythm entropy was introduced to analyze the complexity of the cognitive activity. The phase-lock values related to the parietal and frontal lobes were calculated, and their temporal synchronization was discussed. Finally, the motor imagery EEG data was classified by CNNs, and the accuracy was discussed based on the analysis results.ResultFor the young and elderly, ERD was observed in C3 and C4 channels, and their fatigue-sensitive channels in the parietal region were slightly different. During the experiment, the rhythm entropy of the frontal lobe showed a decreasing trend with time for most of the young subjects, while there was an increasing trend for most of the older ones. Using the CNN classification method, the elderly achieved around 70% of the average classification accuracy, which is almost the same for the young adults.ConclusionCompared with the young adults, the elderly are less affected by the level of cognitive fatigue during motor imagery, but the classification accuracy of motor imagery data in the elderly may be slightly lower than that in young persons. At the same time, the deep learning method also provides a potentially feasible option for the application of motor-imagery BCI (MI-BCI) in the elderly by considering the ERD and fatigue phenomenon together.

State-of-the-Art Versus Deep Learning: A Comparative Study of Motor Imagery Decoding Techniques

State-of-the-art techniques (SOTA) for motor imagery decoding have largely involved the use of common spatial patterns (CSP) and power spectral density (PSD), for feature extraction. Other frequency transforms, such as wavelets and empirical mode decomposition (EMD) have also been used but the aforementioned two have been the most popular. For classification, linear discriminant analysis (LDA) and support vector machines (SVM) have been mostly used. It is, however, worth investigating other approaches, such as deep learning, which offer a potential for improvement, but are not yet mainstream. Deep learning techniques based on neural networks (NNs) have been underexplored in motor imagery processing. Considering their success in other fields, which speaks to their potential for obtaining improved results over the SOTA, they should be explored for motor imagery decoding. This study takes a comparative approach in the use of deep learning as compared with the SOTA. From our findings, we infer that neural networks are suitable for motor imagery decoding and might be preferable over the SOTA. The use of specific feature extraction is also not as necessary as seen with SOTA approaches, though it might offer some gains in performance. Our results show a statistically significant improvement in decoding accuracies, up to 20% increase, with the use of NNs as compared with the SOTA. Also, we conclude that the use of crops for data augmentation might yield better results with shallow architectures as against deeper ones and that there might be other factors affecting the effectiveness of crops, needing further investigation.

Implicit Motor Imagery of the Foot and Hand in People with Achilles Tendinopathy: A Left Right Judgement Study.

To determine if impairment in motor imagery processes is present in Achilles tendinopathy (AT), as demonstrated by a reduced ability to quickly and accurately identify the laterality (left-right judgement) of a pictured limb. Additionally, this study aimed to use a novel data pooling approach to combine data collected at 3 different sites via meta-analytical techniques that allow exploration of heterogeneity. Multi-site case-control study. Three independent studies with similar protocols were conducted by separate research groups. Each study-site evaluated left/right judgement performance for images of feet and hands using Recognise© software and compared performance between people with AT and healthy controls. Results from each study-site were independently collated, then combined in a meta-analysis. In total, 126 participants (40 unilateral, 22 bilateral AT cases, 61 controls) were included. There were no differences between AT cases and controls for hand image accuracy and reaction time. Contrary to the hypothesis, there were no differences in performance between those with AT and controls for foot image reaction time, however there were conflicting findings for foot accuracy, based on four separate analyses. There were no differences between the affected and unaffected sides in people with unilateral AT. Impairments in motor imagery performance for hands were not found in this study, and we found inconsistent results for foot accuracy. This contrasts to studies in persistent pain of limbs, face and knee osteoarthritis, and suggests that differences in pathoetiology or patient demographics may uniquely influence proprioceptive representation.

Pain, No Gain: Acute Pain Interrupts Motor Imagery Processes and Affects Mental Training-Induced Plasticity.

Pain influences both motor behavior and neuroplastic adaptations induced by physical training. Motor imagery (MI) is a promising method to recover motor functions, for instance in clinical populations with limited endurance or concomitant pain. However, the influence of pain on the MI processes is not well established. This study investigated whether acute experimental pain could modulate corticospinal excitability assessed at rest and during MI (Exp. 1) and limit the use-dependent plasticity induced by MI practice (Exp. 2). Participants imagined thumb movements without pain or with painful electrical stimulations applied either on digit V or over the knee. We used transcranial magnetic stimulation to measure corticospinal excitability at rest and during MI (Exp. 1) and to evoke involuntary thumb movements before and after MI practice (Exp. 2). Regardless of its location, pain prevented the increase of corticospinal excitability that is classically observed during MI. In addition, pain blocked use-dependent plasticity following MI practice, as testified by a lack of significant posttraining deviations. These findings suggest that pain interferes with MI processes, preventing the corticospinal excitability facilitation needed to induce use-dependent plasticity. Pain should be carefully considered for rehabilitation programs using MI to restore motor function.

Alpha and high gamma phase amplitude coupling during motor imagery and weighted cross-frequency coupling to extract discriminative cross-frequency patterns

Motor imagery modulates specific neural oscillations like actual movement does. Representatively, suppression of the alpha power (e.g., event-related desynchronization [ERD]) is the typical pattern of motor imagery in the motor cortex. However, in addition to this amplitude-based feature, the coupling across frequencies includes important information about the brain functions and the existence of such complex information has been reported in various invasive studies. Yet, the interaction across multiple frequencies during motor imagery processing is still unclear and has not been widely studied, particularly concerning the non-invasive signals. In this study, we provide empirical evidence of the comodulation between the phase of alpha rhythm and the amplitude of high gamma rhythm during the motor imagery process. We used electroencephalography (EEG) in our investigation during the imagination of left- or right-hand movement recorded from 52 healthy subjects, and quantified the ERD of alpha and phase-amplitude coupling (PAC) which is a relative change of modulation index to the base line period (before the cue). As a result, we found that the coupling between the phase of alpha (8–12 Hz) and the amplitude of high gamma (70–120 Hz) and this PAC decreases during motor imagery and then rebounds to the baseline like alpha ERD (r = 0.29 to 0.42). This correlation between PAC and ERD was particularly stronger in the ipsilateral area. In addition, trials that demonstrated higher alpha power during the ready period (before the cue) showed a larger ERD during motor imagery and similarly, trials with higher modulation index during the ready period yielded a greater decrease in PAC during imagery. In the classification analysis, we found that the effective phase frequency that showed better decoding accuracy in left and right-hand imagery, varied across subjects. Motivated by result, we proposed a weighted cross-frequency coupling (WCFC) method that extracts the maximal discriminative feature by combining band power and CFC. In the evaluation, WCFC with only two electrodes yielded a performance comparable to the conventional algorithm with 64 electrodes in classifying left and right-hand motor imagery. These results indicate that the phase-amplitude frequency plays an important role in motor imagery, and that optimizing this frequency ranges is crucial for extracting information features to decode the motor imagery types.

Imagining handwriting movements in a usual or unusual position: effect of posture congruency on visual and kinesthetic motor imagery

Motor imagery has been used in training programs to improve the performance of motor skills. Handwriting movement may benefit from motor imagery training. To optimize the efficacy of this kind of training, it is important to identify the factors that facilitate the motor imagery process for handwriting movements. Several studies have shown that motor imagery is more easily achieved when there is maximum compatibility between the actual posture and the imagined movement. We, therefore, examined the effect of posture congruency on visual and kinesthetic motor imagery for handwriting movements. Adult participants had to write and imagine writing a sentence by focusing on the evocation of either the kinesthetic or visual consequences of the motion. Half the participants performed the motor imagery task in a congruent posture (sitting with a hand ready for writing), and half in an incongruent one (standing with arms crossed behind the back and fingers spread wide). The temporal similarity between actual and imagined movement times and the vividness of the motor imagery were evaluated. Results revealed that temporal similarity was stronger in the congruent posture condition than in the incongruent one. Furthermore, in the incongruent posture condition, participants reported greater difficulty forming a precise kinesthetic motor image of themselves writing than a visual image, whereas no difference was observed in the congruent posture condition. Taken together, our results show that postural information is taken into account during the mental simulation of handwriting movements. The implications of these findings for guiding the design of motor imagery training are discussed.

Neurosurgical lesions to sensorimotor cortex do not impair action verb processing

There is ongoing debate regarding the role that sensorimotor regions play in conceptual processing, with embodied theories supporting their direct involvement in processing verbs describing body part movements. Patient lesion studies examining a causal role for sensorimotor activation in conceptual task performance have suffered the caveat of lesions being largely diffuse and extensive beyond sensorimotor cortices. The current study addresses this limitation in reporting on 20 pre-operative neurosurgical patients with focal lesion to the pre- and post-central area corresponding to somatotopic representations. Patients were presented with a battery of neuropsychological tests and experimental tasks tapping into motor imagery and verbal conceptual verb processing in addition to neurophysiological measures including DTI, fMRI, and MEP being measured. Results indicated that left tumor patients who presented with a lesion at or near somatotopic hand representations performed significantly worse on the mental rotation hand task and that performance correlated with MEP amplitudes in the upper limb motor region. Furthermore, performance on tasks of verbal processing was within the normal range. Taken together, while our results evidence the involvement of the motor system in motor imagery processes, they do not support the embodied view that sensorimotor regions are necessary to tasks of action verb processing.

Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces.

One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issues in brain-computer interface research. In conventional approaches, ineffective decoding of features and high complexity of algorithms often lead to unsatisfactory performance. A novel method for the recognition of motor imagery tasks is developed based on employing a modified S-transforms for spectro-temporal representation to characterize the behavior of electrocorticogram activities. A classifier is trained by using a support vector machine, and an optimized wrapper approach is applied to guide selection to implement the representation selection obtained. A channel selection algorithm optimizes the wrapper approach by adding a cross-validation step, which effectively improves the classification performance. The modified S-transform can accurately capture event-related desynchronization/event-related synchronization phenomena and can effectively locate sensorimotor rhythm information. The optimized wrapper approach used in this scheme can effectively reduce the feature dimension and improve algorithm efficiency. The method is evaluated on a public electrocorticogram dataset with a recognition accuracy of 98% and an information transfer rate of 0.8586 bit/trial. To verify the effect of the channel selection, both electrocorticogram and electroencephalogram data are experimentally analyzed. Furthermore, the computational efficiency of this scheme demonstrates its potential for online brain-computer interface systems in future cognitive tasks.

The effects of varying levels of mental workload on motor imagery based brain-computer interface

As one of the most applied EEG-based paradigms, motor imagery based brain-computer interface (MI-BCI) is used not only to control external devices, but also to help hemiplegic patients to reconstruct impaired motor function. However, in practical application of MI-BCI, users often face more varied external environments and complex cognitive activities, which could induce a high mental workload. This paper studied the effects of mental workload on motor imagery by designing a parallel task containing required motor and N-back task, taking motor execution as comparison. The experimental results showed that high mental workloads promoted the cognitive-motor process of motor imagery and restrained motor execution. Besides, the classification performance of MI-BCI was evaluated and compared at different mental workload levels between motor imagery and motor idle state. We also verified the possibility of detecting mental workload levels during motor imagery in offline analysis. The paper contributed to a wide range of MI-BCI applications and by exploring the cognitive-motor mechanism in motor imagery and execution.

The effects of varying levels of mental workload on motor imagery based brain-computer interface

As one of the most applied EEG-based paradigms, motor imagery based brain-computer interface (MI-BCI) is used not only to control external devices, but also to help hemiplegic patients to reconstruct impaired motor function. However, in practical application of MI-BCI, users often face more varied external environments and complex cognitive activities, which could induce a high mental workload. This paper studied the effects of mental workload on motor imagery by designing a parallel task containing required motor and N-back task, taking motor execution as comparison. The experimental results showed that high mental workloads promoted the cognitive-motor process of motor imagery and restrained motor execution. Besides, the classification performance of MI-BCI was evaluated and compared at different mental workload levels between motor imagery and motor idle state. We also verified the possibility of detecting mental workload levels during motor imagery in offline analysis. The paper contributed to a wide range of MI-BCI applications and by exploring the cognitive-motor mechanism in motor imagery and execution.

A Comparative Study of Linear and Non-Linear Classifiers in Sensory Motor Imagery Based Brain Computer Interface

Over the period of 4–5 decades of the field electrophysiology on neural signal events in single cell recording, it can be concluded that there is production of far field potentials containing near synchronous field patterns might reach at the scalp electrodes. These neural signals then can be analyzed and converted into the control signals for computers and other electronic devices. Electroencephalography (EEG) is a method to acquire these neural signals from the scalp of human brain. EEG signals are simple, economical and have high temporal resolution properties. These properties make it advantageous to use widely in the medical as well as non-medical applications. The event related synchronization and desynchronization (ERS/ERD) pattern present in EEG during sensory motor imagery (SMI) process over the cortical area is an important feature to take BCI towards realistic approach. The accurate classification of these ERS/ERD pattern present in EEG signal is dependent on classification accuracy of different classifiers. So, the objective of the paper is to analyze the classification accuracy of linear and non-linear classifiers used for BCI system design. This paper also presents the comparative study of linear (Linear discriminant analysis and Support Vector Machine) and non-linear classifiers (Bayesian and Radial Basis Function-Support Vector Machine) using BCI competition IV dataset 2a. The result concluded that linear classifiers (LDA and SVM) have outperformed the non-linear classifiers on EEG data with the average performance across subjects 75.26±12.23, 72.42±11.12.

Dynamic motor imagery mentally simulates uncommon real locomotion better than static motor imagery both in young adults and elderly.

A new form of Motor Imagery (MI), called dynamic Motor Imagery (dMI) has recently been proposed. The dMI adds to conventional static Motor Imagery (sMI) the presence of simultaneous actual movements partially replicating those mentally represented. In a previous research conducted on young participants, dMI showed to be temporally closer than sMI in replicating the real performance for some specific locomotor conditions. In this study, we evaluated if there is any influence of the ageing on dMI. Thirty healthy participants were enrolled: 15 young adults (27.1±3.8 y.o.) and 15 older adults (65.9±9.6y.o.). The performance time and the number of steps needed to either walk to a target (placed at 10m from participants) or to imagine walking to it, were assessed. Parameters were measured for sMI, dMI and real locomotion (RL) in three different locomotor conditions: forward walking (FW), backward walking (BW), and lateral walking (LW). Temporal performances of sMI and dMI did not differ between RL in the FW, even if significantly different to each other (p = 0.0002). No significant differences were found for dMI with respect to RL for LW (p = 0.140) and BW (p = 0.438), while sMI was significantly lower than RL in LW (p<0.001). The p-value of main effect of age on participants’ temporal performances was p = 0.055. The interaction between age and other factors such as the type of locomotion (p = 0.358) or the motor condition (p = 0.614) or third level interaction (p = 0.349) were not statistically significant. Despite a slight slowdown in the performance of elderly compared to young participants, the temporal and spatial accuracy was better in dMI than sMI in both groups. Motor imagery processes may be strengthened by the feedback generated through dMI, and this effect appears to be unaffected by age.

A Blockchain-Based Non-Invasive Cyber-Physical Occupational Therapy Framework: BCI Perspective

Although ElectroEncephaloGram (EEG) signals allow subjects suffering from neuromuscular disorders to interface their brains with the cyber-physical world, occupational therapy can be enhanced with the introduction of further modalities better assist the disabled person. In this paper, we propose an in-home occupational therapy environment, which leverages a rich set of occupational therapy-related activity recognition modalities, namely, EEG signals to understand brain activity, ElectroMyoGram (EMG) signals for muscle activity, gesture-tracking sensors for forward and inverse kinematics activities, and smart home appliance control sensors. To support a wide variety of disabled people’s in-home occupational therapy, we have incorporated both selective attention and motor imagery processes for mapping a mental command with that of an occupational therapy-related command within a serious game environment. To attain higher accuracy and to avoid a higher number of false positives, a subject is first recommended to use a selective attention-based serious game in which a digital avatar of the subject acting as a model therapist will guide the therapy session. Once familiar with the generation of proper motor imagery, an advanced user can use self-paced motor imagery signals to perform occupational therapy activities within the serious game environment. The occupational therapy consists of a serious game environment in which smart home appliances are mapped with therapeutic activities through forward and inverse kinematics. The therapy data has been secured through blockchain and off-chain-based distributed repositories. The test results show the viability of using the framework in a clinical environment.