Investigating Priming Effects of Physical Practice on Motor Imagery-Induced Event-Related Desynchronization

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

Introduction

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

Methods

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

Results

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

Conclusion

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

Enhancing motor imagery detection efficacy using multisensory virtual reality priming

Brain-computer interfaces (BCI) have been developed to allow users to communicate with the external world by translating brain activity into control signals. Motor imagery (MI) has been a popular paradigm in BCI control where the user imagines movements of e.g., their left and right limbs and classifiers are then trained to detect such intent directly from electroencephalography (EEG) signals. For some users, however, it is difficult to elicit patterns in the EEG signal that can be detected with existing features and classifiers. As such, new user control strategies and training paradigms have been highly sought-after to help improve motor imagery performance. Virtual reality (VR) has emerged as one potential tool where improvements in user engagement and level of immersion have shown to improve BCI accuracy. Motor priming in VR, in turn, has shown to further enhance BCI accuracy. In this pilot study, we take the first steps to explore if multisensory VR motor priming, where haptic and olfactory stimuli are present, can improve motor imagery detection efficacy in terms of both improved accuracy and faster detection. Experiments with 10 participants equipped with a biosensor-embedded VR headset, an off-the-shelf scent diffusion device, and a haptic glove with force feedback showed that significant improvements in motor imagery detection could be achieved. Increased activity in the six common spatial pattern filters used were also observed and peak accuracy could be achieved with analysis windows that were 2 s shorter. Combined, the results suggest that multisensory motor priming prior to motor imagery could improve detection efficacy.

Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm

Motor imagery (MI) refers to the mental rehearsal of movement in the absence of overt motor action, which can activate or inhibit cortical excitability. EEG mu/beta oscillations recorded over the human motor cortex have been shown to be consistently suppressed during both the imagination and performance of movements, although the specific effect on brain function remains to be confirmed. In this study, Granger causality (GC) was used to construct the brain functional network of subjects during motor imagery and resting state based on EEG in order to explore the effects of motor imagery on brain function. Parameters of the brain functional network were compared and analyzed, including degree, clustering coefficient, characteristic path length and global efficiency of EEG mu/beta rhythm in different states. The results showed that the clustering coefficient and efficiency of EEG mu/beta rhythm decreased significantly during motor imagery (p < 0.05), while degree distribution and characteristic path length increased significantly (p < 0.05), mainly concentrated in the frontal lobe and sensorimotor area. For the resting state after motor imagery, the changes of brain functional characteristics were roughly similar to those of the task state. Therefore, it is concluded that motor imagery plays an important role in activation of cortical excitability.

The Impact of Context on EEG Motor Imagery Neurofeedback and Related Motor Domains

Neurofeedback (NF) is a versatile non-invasive neuromodulation technique. In combination with motor imagery (MI), NF has considerable potential for enhancing motor performance or supplementing motor rehabilitation. However, not all users achieve reliable NF control. While research has focused on various brain signal properties and the optimisation of signal processing to solve this issue, the impact of context, i.e. the conditions in which NF motor tasks occur, is comparatively unknown. We review current research on the impact of context on MI NF and related motor domains. We identify long-term factors that act at the level of the individual or of the intervention, and short-term factors, with levels before/after and during a session. The reviewed literature indicates that context plays a significant role. We propose considering context factors as well as within-level and across-level interactions when studying MI NF.

Revisiting the acute effects of resistance exercise on motor imagery ability

Motor imagery (MI) shares psychological and physiological similarities with the physical practice of the same action. Yet, it remains unclear whether fatigue elicited by exercise impairs MI ability. Fourteen participants performed MI of a self-paced walking sequence of 22 m before and after a resistance exercise eliciting muscle fatigue from upper and lower limbs, selectively. We indexed MI ability using psychometric and behavioral methods. Electromyography of the quadriceps was also recorded during physical practice trials of the walking sequence. For both experimental conditions, we recorded improved temporal congruence between MI and physical practice of the walking sequence (9.89 %, 95 % CI [7.03, 12.75], p < 0.01). Vividness decreased immediately after the fatiguing exercise (6.35 %, 95 % CI [5.18, 7.51], p < 0.05), before rapidly returning to pre-fatigue values during recovery trials. The results challenge the hypothesis of an effect of acute fatigue elicited by a resistance exercise on MI ability, i.e. restricted to MI tasks focusing fatigued effectors. The beneficial effects of fatigue conditions on the psychometric and behavioral indexes of MI ability are discussed in the broader context of psychobiological fatigue models linking perceived exertion with the reallocation of attentional resources. The general perception of fatigue, rather than local muscle fatigue, appeared linked to the acute effects of resistance exercise on MI ability.

Mu oscillations and motor imagery performance: A reflection of intra-individual success, not inter-individual ability

Mu oscillations (8-13 Hz), recorded over the human motor cortex, have been shown to consistently suppress during both the imagination and performance of movements; however, its functional significance in the imagery process is currently unclear. Here we examined human electroencephalographic (EEG) oscillations in the context of motor imagery performance as measured by imagery success within participants and imagery ability between participants. We recorded continuous EEG activity while participants performed the Test of Ability in Movement Imagery (TAMI), an objective test of motor imagery task. Results demonstrated that mu oscillatory activity significantly decreased during successful as compared to unsuccessful imagery trials. However, the extent of reduction in mu oscillations did not correlate with overall imagery ability as measured by the total TAMI score. These findings provide further support for the involvement of mu oscillations in indexing motor imagery performance and suggest that mu oscillations may reflect important processes related to imagery accuracy, processes likely related to those underlying overt motor production and motor understanding.

Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling

Motor imagery-based brain–computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines—but does not perform—a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as “BCI-inefficiency,” in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of psychological questionnaires and pre-experimental resting-state taken over two sessions on different days. A dynamic causal model was used to calculate the coupling strengths between brain regions with directionality. Specifically, we investigated the motor network in resting-state, including the dorsolateral prefrontal cortex, which performs motor planning. As a result, we observed a significant difference in the connectivity strength from the supplementary motor area to the right dorsolateral prefrontal cortex between the low- and high-MI performance groups. This coupling, measured in the resting-state, is significantly stronger in the high-MI performance group than the low-MI performance group. The connection strength is positively correlated with MI-BCI performance (Session 1: r = 0.54; Session 2: r = 0.42). We also predicted MI performance using linear regression based on this connection (r-squared = 0.31). The proposed predictors, based on dynamic causal modeling, can develop new strategies for improving BCI performance. These findings can further our understanding of BCI-inefficiency and help BCI users to lower costs and save time.