Subjective Vividness of Kinesthetic Motor Imagery Is Associated With the Similarity in Magnitude of Sensorimotor Event-Related Desynchronization Between Motor Execution and Motor Imagery

Characterization and classification of kinesthetic motor imagery levels

Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

Motor imagery ability scores are related to cortical activation during gait imagery

Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the θ, α, and β band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson's and Spearman's coefficients to correlate MI ability scores and average ERD power (avgERD). Positive correlations were identified between VMIQ and avgERD of the middle cingulum in the β band and with avgERD of the left insula, right precentral area, and right middle occipital region in the θ band. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.

Study of the Correlation between the Motor Ability of the Individual Upper Limbs and Motor Imagery Induced Neural Activities

Motor imagery based brain-computer interfaces (MI-BCIs) have excellent application prospects in motor enhancement and rehabilitation. However, MI-induced electroencephalogram features applied to MI-BCI usually vary from person to person. This study aimed to investigate whether the motor ability of the individual upper limbs was associated with these features, which helps understand the causes of inter-subject variability. We focused on the behavioral and psychological factors reflecting motor abilities. We first obtained the behavioral scale scores from Edinburgh Handedness Questionnaire, Maximum Grip Strength Test, and Purdue Pegboard Test assessments to evaluate the motor execution ability. We also required the subjects to complete the psychological Movement Imagery Questionnaire-3 estimate, representing MI ability. Then we recorded EEG signals from all twenty-two subjects during MI tasks. Pearson correlation coefficient and stepwise regression were used to analyze the relationships between MI-induced relative event-related desynchronization (rERD) patterns and motor abilities. Both Purdue Pegboard Test and Movement Imagery Questionnaire-3 scores had significant correlations with MI-induced neural oscillation patterns. Notably, the Purdue Pegboard Test of the left hand had the most significant correlation with the alpha rERD. The results of stepwise multiple regression analysis showed that the Purdue Pegboard Test and Movement Imagery Questionnaire-3 could best predict the MI-induced rERD. The results demonstrate that hand dexterity and fine motor coordination are significantly related to MI-induced neural activities. In addition, the method of imagining is also relevant to MI features. Therefore, this study is meaningful for understanding individual differences and the design of user-centered MI-BCI.

Handedness effects on imagery of dominant- versus non-dominant-hand movements: An electroencephalographic investigation

Mental representations of our bodies are thought to influence how we interact with our surroundings. We can examine these mental representations through motor imagery, the imagination of movement using scalp EEG recordings. The visual modality of motor imagery emphasises 'seeing' the imagined movement and is associated with increased activity in the alpha rhythm (8-14 Hz) measured over the occipital regions. The kinaesthetic modality emphasises 'feeling' the movement and is associated with decreased activity in the mu rhythm (8-14 Hz) measured over the sensorimotor cortices. These two modalities can be engaged in isolation or together. We recorded EEG activity while 37 participants (17 left-hand dominant) completed an objective hand motor imagery task. Left-handers exhibited significant activity differences between occipital and motor regions only during imagery of right-hand (non-dominant-hand) movements. This difference was primarily driven by less oscillatory activity in the mu rhythm, which may reflect a shift in imagery strategy wherein participants placed more effort into generating the kinaesthetic sensations of non-dominant-hand imagery. Spatial features of 8-14 Hz activity generated from principal component analysis (PCA) provide further support for a strategy shift. Right-handers also exhibited significant differences between alpha and mu activity during imagery of non-dominant movements. However, this difference was not primarily driven by either rhythm, and no differences were observed in the group's PCA results. Together, these findings indicate that individuals imagine movement differently when it involves their dominant versus non-dominant hand, and left-handers may be more flexible in their motor imagery strategies.

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

Introduction

In the past, various techniques have been used to improve motor imagery (MI), such as immersive virtual-reality (VR) and kinesthetic rehearsal. While electroencephalography (EEG) has been used to study the differences in brain activity between VR-based action observation and kinesthetic motor imagery (KMI), there has been no investigation into their combined effect. Prior research has demonstrated that VR-based action observation can enhance MI by providing both visual information and embodiment, which is the perception of oneself as part of the observed entity. Additionally, KMI has been found to produce similar brain activity to physically performing a task. Therefore, we hypothesized that utilizing VR to offer an immersive visual scenario for action observation while participants performed kinesthetic motor imagery would significantly improve cortical activity related to MI.

Methods

In this study, 15 participants (9 male, 6 female) performed kinesthetic motor imagery of three hand tasks (drinking, wrist flexion-extension, and grabbing) both with and without VR-based action observation.

Results

Our results indicate that combining VR-based action observation with KMI enhances brain rhythmic patterns and provides better task differentiation compared to KMI without action observation.

Discussion

These findings suggest that using VR-based action observation alongside kinesthetic motor imagery can improve motor imagery performance.

The Effect of Tactile Imagery Training on Reaction Time in Healthy Participants

BACKGROUND

Reaction time is an important measure of sensorimotor performance and coordination and has been shown to improve with training. Various training methods have been employed in the past to improve reaction time. Tactile imagery (TI) is a method of mentally simulating a tactile sensation and has been used in brain-computer interface applications. However, it is yet unknown whether TI can have a learning effect and improve reaction time.

OBJECTIVE

The purpose of this study was to investigate the effect of TI on reaction time in healthy participants.

METHODS

We examined the reaction time to vibratory stimuli before and after a TI training session in an experimental group and compared the change in reaction time post-training with pre-training in the experimental group as well as the reaction time in a control group. A follow-up evaluation of reaction time was also conducted.

RESULTS

The results showed that TI training significantly improved reaction time after TI compared with before TI by approximately 25% (pre-TI right-hand mean ± SD: 456.62 ± 124.26 ms, pre-TI left-hand mean ± SD: 448.82 ± 124.50 ms, post-TI right-hand mean ± SD: 340.32 ± 65.59 ms, post-TI left-hand mean ± SD: 335.52 ± 59.01 ms). Furthermore, post-training reaction time showed significant reduction compared with the control group and the improved reaction time had a lasting effect even after four weeks post-training.

CONCLUSION

These findings indicate that TI training may serve as an alternate imagery strategy for improving reaction time without the need for physical practice.

The Effects of Subthreshold Vibratory Noise on Cortical Activity During Motor Imagery

Previous studies have demonstrated that both visual and proprioceptive feedback play vital roles in mental practice of movements. Tactile sensation has been shown to improve with peripheral sensory stimulation via imperceptible vibratory noise by stimulating the sensorimotor cortex. With both proprioception and tactile sensation sharing the same population of posterior parietal neurons encoding within high-level spatial representations, the effect of imperceptible vibratory noise on motor imagery-based brain-computer interface is unknown. The objective of this study was to investigate the effects of this sensory stimulation via imperceptible vibratory noise applied to the index fingertip in improving motor imagery-based brain-computer interface performance. Fifteen healthy adults (nine males and six females) were studied. Each subject performed three motor imagery tasks, namely drinking, grabbing, and flexion-extension of the wrist, with and without sensory stimulation while being presented a rich immersive visual scenario through a virtual reality headset. Results showed that vibratory noise increased event-related desynchronization during motor imagery compared with no vibration. Furthermore, the task classification percentage was higher with vibration when the tasks were discriminated using a machine learning algorithm. In conclusion, subthreshold random frequency vibration affected motor imagery-related event-related desynchronization and improved task classification performance.

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

Background

The 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.

Objectives

This 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.

Methods

A 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.

Result

For 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.

Conclusion

Compared 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.

Relationship between the vividness of motor imagery and physical function in patients with subacute hemiplegic stroke: a cross-sectional preliminary study

PURPOSE

The study aimed to clarify whether the vividness of motor imagery is related to lower limb function and walking ability in patients with hemiplegic stroke.

MATERIALS AND METHODS

The study was a cross-sectional preliminary study. The subjects were 15 patients with hemiplegic stroke. The vividness of motor imagery was assessed using the kinesthetic and visual imagery questionnaire. The kinesthetic imagery (KI) involves the sensation of one's own movement, whereas the visual imagery (VI) involves the imagination of a third-person performing the self-movement. Their physical functions were assessed using the Brunnstrom recovery stage, stroke impairment assessment set, 10-m maximum walking speed test, and functional independence measure. KI and VI were compared using the t test. Correlation analysis was performed between KI or VI and various variables as well as between the motor imagery gap (difference between KI and VI) and various variables.

RESULTS

KI was significantly lower than VI (p < .01). KI was correlated not only with lower limb function (r = 0.68) but also with walking speed (r = -0.64). The motor imagery gap was correlated with hip joint function (r = -0.53).

CONCLUSIONS

KI and motor imagery gap were associated with lower limb function and walking ability.

Gymnasts' Ability to Modulate Sensorimotor Rhythms During Kinesthetic Motor Imagery of Sports Non-specific Movements Superior to Non-gymnasts

Previous psychological studies using questionnaires have consistently reported that athletes have superior motor imagery ability, both for sports-specific and for sports-non-specific movements. However, regarding motor imagery of sports-non-specific movements, no physiological studies have demonstrated differences in neural activity between athletes and non-athletes. The purpose of this study was to examine the differences in sensorimotor rhythms during kinesthetic motor imagery (KMI) of sports-non-specific movements between gymnasts and non-gymnasts. We selected gymnasts as an example population because they are likely to have particularly superior motor imagery ability due to frequent usage of motor imagery, including KMI as part of daily practice. Healthy young participants (16 gymnasts and 16 non-gymnasts) performed repeated motor execution and KMI of sports-non-specific movements (wrist dorsiflexion and shoulder abduction of the dominant hand). Scalp electroencephalogram (EEG) was recorded over the contralateral sensorimotor cortex. During motor execution and KMI, sensorimotor EEG power is known to decrease in the α- (8–15 Hz) and β-bands (16–35 Hz), referred to as event-related desynchronization (ERD). We calculated the maximal peak of ERD both in the α- (αERDmax) and β-bands (βERDmax) as a measure of changes in corticospinal excitability. αERDmax was significantly greater in gymnasts, who subjectively evaluated their KMI as being more vivid in the psychological questionnaire. On the other hand, βERDmax was greater in gymnasts only for shoulder abduction KMI. These findings suggest gymnasts' signature of flexibly modulating sensorimotor rhythms with no movements, which may be the basis of their superior ability of KMI for sports-non-specific movements.

Enhancement of capability for motor imagery using vestibular imbalance stimulation during brain computer interface

Objective.Motor imagery (MI), based on the theory of mirror neurons and neuroplasticity, can promote motor cortical activation in neurorehabilitation. The strategy of MI based on brain-computer interface (BCI) has been used in rehabilitation training and daily assistance for patients with hemiplegia in recent years. However, it is difficult to maintain the consistency and timeliness of receiving external stimulation to neural activation in most subjects owing to the high variability of electroencephalogram (EEG) representation across trials/subjects. Moreover, in practical application, MI-BCI cannot highly activate the motor cortex and provide stable interaction owing to the weakness of the EEG feature and lack of an effective mode of activation.Approach.In this study, a novel hybrid BCI paradigm based on MI and vestibular stimulation motor imagery (VSMI) was proposed to enhance the capability of feature response for MI. Twelve subjects participated in a group of controlled experiments containing VSMI and MI. Three indicators, namely, activation degree, timeliness, and classification accuracy, were adopted to evaluate the performance of the task.Main results.Vestibular stimulation could significantly strengthen the suppression ofαandβbands of contralateral brain regions during MI, that is, enhance the activation degree of the motor cortex (p< 0.01). Compared with MI, the timeliness of EEG feature-response achieved obvious improvements in VSMI experiments. Moreover, the averaged classification accuracy of VSMI and MI was 80.56% and 69.38%, respectively.Significance.The experimental results indicate that specific vestibular activity contributes to the oscillations of the motor cortex and has a positive effect on spontaneous imagery, which provides a novel MI paradigm and enables the preliminary exploration of sensorimotor integration of MI.

Effects of visual-motor illusion on functional connectivity during motor imagery

This study aimed to verify whether visual-motor illusion changes the functional connectivity during kinesthetic motor imagery and the vividness of kinesthetic motor imagery. Twelve right-handed healthy adults participated in this study. All participants randomly performed both the illusion and observation conditions in 20 min, respectively. Illusion condition was induced kinesthetic illusion by viewing own finger movement video. Observation condition was observed own finger movement video. Before and after each condition, the brain activity of kinesthetic motor imagery was measured using functional near-infrared spectroscopy. The measure of brain activity under kinesthetic motor imagery was executed in five sets using block design. Under the kinesthetic motor imagery, participants were asked to imagine the movement of their right finger. Functional connectivity was analyzed during the kinesthetic motor imagery. In addition, after performing the task under kinesthetic motor imagery, the vividness of the kinesthetic motor imagery was measured using a visual analog scale. Furthermore, after each condition, the degree of kinesthetic illusion and sense of body ownership measured based on a seven-point Likert scale. Our results indicated that the functional connectivity during kinesthetic motor imagery was changed in the frontal-parietal network of the right hemisphere. The vividness of the kinesthetic motor imagery was significantly higher with the illusion condition compared with the observation condition. The degree of kinesthetic illusion and sense of body ownership were significantly higher with the illusion condition compared with the observation condition. In conclusion, the visual-motor illusion changes the functional connectivity during kinesthetic motor imagery and influences the vividness of kinesthetic motor imagery. The visual-motor illusion provides evidence that it improves motor imagery ability. VMI may be used in patients with impaired motor imagery.

Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training

By performing motor-imagery tasks, for example, imagining hand movements, Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) users can control digital technologies, for example, neuroprosthesis, using their brain activity only. MI-BCI users need to train, usually using a unimodal visual feedback, to produce brain activity patterns that are recognizable by the system. The literature indicates that multimodal vibrotactile and visual feedback is more effective than unimodal visual feedback, at least for short term training. However, the multi-session influence of such multimodal feedback on MI-BCI user training remained unknown, so did the influence of the order of presentation of the feedback modalities. In our experiment, 16 participants trained to control a MI-BCI during five sessions with a realistic visual feedback and five others with both a realistic visual feedback and a vibrotactile one. training benefits from a multimodal feedback, in terms of performances and self-reported mindfulness. There is also a significant influence of the order presentation of the modality. Participants who started training with a visual feedback had higher performances than those who started training with a multimodal feedback. We recommend taking into account the order of presentation for future experiments assessing the influence of several modalities of feedback.

Progress in Brain Computer Interface: Challenges and Opportunities

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Characterizing the Action-Observation Network Through Functional Near-Infrared Spectroscopy: A Review

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that has undergone tremendous growth over the last decade due to methodological advantages over other measures of brain activation. The action-observation network (AON), a system of brain structures proposed to have “mirroring” abilities (e.g., active when an individual completes an action or when they observe another complete that action), has been studied in humans through neural measures such as fMRI and electroencephalogram (EEG); however, limitations of these methods are problematic for AON paradigms. For this reason, fNIRS is proposed as a solution to investigating the AON in humans. The present review article briefly summarizes previous neural findings in the AON and examines the state of AON research using fNIRS in adults. A total of 14 fNIRS articles are discussed, paying particular attention to methodological choices and considerations while summarizing the general findings to aid in developing better protocols to study the AON through fNIRS. Additionally, future directions of this work are discussed, specifically in relation to researching AON development and potential multimodal imaging applications.

Characterization of kinesthetic motor imagery compared with visual motor imageries

Motor imagery (MI) is the only way for disabled subjects to robustly use a robot arm with a brain-machine interface. There are two main types of MI. Kinesthetic motor imagery (KMI) is proprioceptive (OR somato-) sensory imagination and Visual motor imagery (VMI) represents a visualization of the corresponding movement incorporating the visual network. Because these imagery tactics may use different networks, we hypothesized that the connectivity measures could characterize the two imageries better than the local activity. Electroencephalography data were recorded. Subjects performed different conditions, including motor execution (ME), KMI, VMI, and visual observation (VO). We tried to classify the KMI and VMI by conventional power analysis and by the connectivity measures. The mean accuracies of the classification of the KMI and VMI were 98.5% and 99.29% by connectivity measures (alpha and beta, respectively), which were higher than those by the normalized power (p < 0.01, Wilcoxon paired rank test). Additionally, the connectivity patterns were correlated between the ME-KMI and between the VO-VMI. The degree centrality (DC) was significantly higher in the left-S1 at the alpha-band in the KMI than in the VMI. The MI could be well classified because the KMI recruits a similar network to the ME. These findings could contribute to MI training methods.

The Vividness of Motor Imagery Is Correlated With Corticospinal Excitability During Combined Motor Imagery and Action Observation

The present study aimed to investigate the relationship between motor imagery (MI) assessment (ability and quality) and neurophysiological assessment [transcranial magnetic stimulation (TMS)-induced motor-evoked potentials (MEPs)] during combined MI and action observation (AO; MI + AO). Sixteen subjects completed an MI task playing the piano with both hands, and neurophysiological assessment was performed during the MI task. The Movement Imagery Questionnaire-Revised was adopted to evaluate MI ability, while the visual analogue scale (VAS) was adopted to evaluate MI quality. A TMS pulse was delivered during the MI task, and MEPs were subsequently recorded in the abductor pollicis brevis (APB). We found a significant positive correlation between the VAS score and the TMS-induced MEPs (ρ = 0.497, p < 0.001). These findings suggest that the VAS score could potentially reflect the corticospinal excitability during MI + AO, particularly in complex MI tasks.

Neurofeedback of scalp bi-hemispheric EEG sensorimotor rhythm guides hemispheric activation of sensorimotor cortex in the targeted hemisphere

Oscillatory electroencephalographic (EEG) activity is associated with the excitability of cortical regions. Visual feedback of EEG-oscillations may promote sensorimotor cortical activation, but its spatial specificity is not truly guaranteed due to signal interaction among interhemispheric brain regions. Guiding spatially specific activation is important for facilitating neural rehabilitation processes. Here, we tested whether users could explicitly guide sensorimotor cortical activity to the contralateral or ipsilateral hemisphere using a spatially bivariate EEG-based neurofeedback that monitors bi-hemispheric sensorimotor cortical activities for healthy participants. Two different motor imageries (shoulder and hand MIs) were selected to see how differences in intrinsic corticomuscular projection patterns might influence activity lateralization. We showed sensorimotor cortical activities during shoulder, but not hand MI, can be brought under ipsilateral control with guided EEG-based neurofeedback. These results are compatible with neuroanatomy; shoulder muscles are innervated bihemispherically, whereas hand muscles are mostly innervated contralaterally. We demonstrate the neuroanatomically-inspired approach enables us to investigate potent neural remodeling functions that underlie EEG-based neurofeedback via a BCI.

Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation

Sensorimotor rhythm (SMR)-based brain–computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conducted to develop and validate an SMR-based BCI-FES system with 2-class MI tasks in a single hand (Phase 1), and investigate the feasibility of the system with stroke and traumatic brain injury (TBI) patients (Phase 2). The results of Phase 1 showed that the accuracy of classifying 2-class MIs (approximately 71.25%) was significantly higher than the true chance level, while that of distinguishing voluntary and passive SMRs was not. In Phase 2, where the patients performed goal-oriented tasks in a semi-asynchronous mode, the effects of the FES existence type and adaptive learning on task performance were evaluated. The results showed that adaptive learning significantly increased the accuracy, and the accuracy after applying adaptive learning under the No-FES condition (61.9%) was significantly higher than the true chance level. The outcomes of the present research would provide insight into SMR-based BCI-controlled FES systems that can connect those with motor disabilities (e.g., stroke and TBI patients) to other people by greatly improving their quality of life. Recommendations for future work with a larger sample size and kinesthetic MI were also presented.

Event-related desynchronization of alpha and beta band neural oscillations predicts speech and limb motor timing deficits in normal aging

Normal aging is associated with decline of motor timing mechanisms implicated in planning and execution of movement. Evidence from previous studies has highlighted the relationship between neural oscillatory activities and motor timing processing in neurotypical younger adults; however, it remains unclear how normal aging affects the underlying neural mechanisms of movement in older populations. In the present study, we recorded EEG activities in two groups of younger and older adults while they performed randomized speech and limb motor reaction time tasks cued by temporally predictable and unpredictable sensory stimuli. Our data showed that older adults were significantly slower than their younger counterparts during speech production and limb movement, especially in response to temporally unpredictable sensory stimuli. This behavioral effect was accompanied by significant desynchronization of alpha (7-12 Hz) and beta (13-25 Hz) band neural oscillatory activities in older compared with younger adults, primarily during the preparatory pre-motor phase of responses for speech production and limb movement. In addition, we found that faster motor reaction times in younger adults were significantly correlated with weaker desynchronization of pre-motor alpha and beta band neural activities irrespective of stimulus timing and response modality. However, the pre-motor components of alpha and beta activities were timing-specific in older adults and were more strongly desynchronized in response to temporally predictable sensory stimuli. These findings highlight the role of alpha and beta band neural oscillations in motor timing processing mechanisms and reflect their functional deficits during the planning phase of speech production and limb movement in normal aging.

Electrophysiological Evidences for the Rotational Uncertainty Effect in the Hand Mental Rotation: An ERP and ERS/ERD Study

Rotational uncertainty refers to the fact that the reaction time (RT) for identifying an upright stimulus is longer when the target stimulus is presented in a sequence of stimuli with different orientations (SU condition) than upright stimuli only (AU condition). Up until now, the rotational uncertainty effect has been only revealed by behavior measures, and its underlying neural mechanism remains unclear. In this study, using the hand mental rotation paradigm and electroencephalogram (EEG) recordings, we aimed to find the electrophysiological evidences of the rotational uncertainty from event-related potential (ERP) and event-related (de)synchronization (ERS/ERD) measurements. Compared with the upright hand stimuli in AU condition, the same stimuli in SU condition took longer RT, elicited stronger α-ERD and β-ERD, and evoked larger P100, P300 and the slow wave (SW) from -500 ms to -200 ms before response. In particular, the amplitude of SW difference (i.e., SW_SU - SW_AU) was negatively correlated with the extent of rotational uncertainty effect (i.e., RT_SU - RT_AU), with its source mainly in the right precentral and postcentral gyri, precuneus, and the left inferior parietal lobule. Our results suggested that identifying the upright hand stimuli in SU condition induced more activation of motor networks, and the rotational uncertainty influenced multiple cognitive processes from the early visual processing to the late mental rotation and judging phases. The results implied that in SU condition, subjects might maintain readiness for the next possible mental rotation immediately after the previous response, with more attention to the coming visual stimuli. Even for the upright stimuli, they might still prepare for the mental rotation, and even mentally rotate the stimuli in a minor angle.

Effects of mental and physical orofacial training on pressure pain sensitivity and tongue strength: A single-blind randomized controlled trial

OBJECTIVES

The main objective of this study was to analyze differences on pain pressure thresholds, tongue strength and perceived effort between various orofacial motor exercise training dosages of mental representation training through motor imagery (MI) and action observation (AO), first in isolation and then in combination with real exercise performance.

METHODS

A single-blind randomized controlled trial was designed. 48 asymptomatic individuals were randomized into two groups: Intensive training group (IG) and Moderate training group (MG). Both groups performed a first session of MI and AO of orofacial exercises training and a second session of actual orofacial exercises combined with mental representation training, but with different dosage in terms of series and repetitions. Pain pressure thresholds (PPTs) in the masseter and temporal muscles and tongue muscle strength were the main variables.

RESULTS

Regarding the PPT, ANOVA revealed significant between-group differences, where MG showed a significantly higher PPT than IG at post-day2, with a medium effect size. Both groups showed with-in group differences between pre and post intervention measures in the first session, but only the IG showed differences in the second. Regarding tongue muscle strength, ANOVA revealed significant within-group differences only in MG between the pre-day and post-day first intervention.

CONCLUSION

The results of the present study suggest that movement representation training performed in isolation may have a positive effect on PPTs and tongue muscle strength. In addition, the combination with the actual execution of the exercises could be considered effective, but it is necessary to take into account the training dosage to avoid fatigue responses.

The format of mental imagery: from a critical review to an integrated embodied representation approach

The issue of the format of mental imagery is still an open debate. The classical analogue (depictive)-propositional (descriptive) debate has not provided definitive conclusions. Over the years, the debate has shifted within the frame of the embodied cognition approach, which focuses on the interdependence of perception, cognition and action. Although the simulation approach still retains the concept of representation, the more radical line of the embodied cognition approach emphasizes the importance of action and clearly disregards the concept of representation. In particular, the enactive approach focuses on motor procedures that allow the body to interact with the environment, whereas the sensorimotor approach focuses on the possession and exercise of sensorimotor knowledge about how the sensory input changes as a function of movement. In this review, the embodied approaches are presented and critically discussed. Then, in an attempt to show that the format of mental imagery varies according to the ability and the strategy used to represent information, the role of individual differences in imagery ability (e.g., vividness and expertise) and imagery strategy (e.g., object vs. spatial imagers) is reviewed. Since vividness is mainly associated with perceptual information, reflecting the activation level of specific imagery systems, whereas the preferred strategy used is mainly associated with perceptual (e.g., object imagery) or amodal and motor information (e.g., spatial imagery), the format of mental imagery appears to be based on dynamic embodied representations, depending on imagery abilities and imagery strategies.

Vividness and accuracy: Two independent aspects of motor imagery

Motor imagery is the mental execution of an action without any actual movement. Although numerous studies have utilized questionnaires to evaluate the vividness of motor imagery, it remains unclear whether it is related to the accuracy of motor imagery. To examine the relationship between vividness and accuracy, we investigated brain activity during kinesthetic and visual motor imagery, by using a novel sequential finger-tapping task. We estimated accuracy by measuring the fidelity of the actual performance and evaluated vividness by using a visual analog scale. We found that accuracy of visual motor imagery was correlated with the activity in the left visual cortex, as well as with bilateral sensorimotor regions. In contrast, vividness of visual motor imagery was associated with the activity in the right orbitofrontal cortex. However, there was no correlation in the brain activity between the right orbitofrontal cortex and visuomotor regions or between vividness and accuracy of motor imagery. In addition, we did not find any correlation in the kinesthetic imagery condition. We conclude that vividness of visual motor imagery is associated with the right orbitofrontal cortex and is independent of processes occurring in sensorimotor regions, which would be responsible for the accuracy of visual motor imagery.