Effect of Physical Fatigue Elicited by Continuous and Intermittent Exercise on Motor Imagery Ability

Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain-Computer Interface

Motor imagery-based brain-computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel-Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user's MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies.

Spatial constraints and cognitive fatigue affect motor imagery of walking in people with multiple sclerosis

Motor imagery (MI) is the mental simulation of an action without any overt motor execution. Interestingly, a temporal coupling between durations of real and imagined movements, i.e., the so-called isochrony principle, has been demonstrated in healthy adults. On the contrary, anisochrony has frequently been reported in elderly subjects or those with neurological disease such as Parkinson disease or multiple sclerosis (MS). Here, we tested whether people with MS (PwMS) may have impaired MI when they imagined themselves walking on paths with different widths. When required to mentally simulate a walking movement along a constrained pathway, PwMS tended to overestimate mental movement duration with respect to actual movement duration. Interestingly, in line with previous evidence, cognitive fatigue was found to play a role in the MI of PwMS. These results suggest that investigating the relationship between cognitive fatigue and MI performances could be key to shedding new light on the motor representation of PwMS and providing critical insights into effective and tailored rehabilitative treatments.

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.