Motor imagery helps updating internal models during microgravity exposure

Skilled movements result from a mixture of feedforward and feedback mechanisms conceptualized by internal models. These mechanisms subserve both motor execution and motor imagery. Current research suggests that imagery allows updating feedforward mechanisms, leading to better performance in familiar contexts. Does this still hold in radically new contexts? Here, we test this ability by asking participants to imagine swinging arm movements around shoulder in normal gravity condition and in microgravity in which studies showed that movements slow down. We timed several cycles of actual and imagined arm pendular movements in three groups of subjects during parabolic flight campaign. The first, control, group remained on the ground. The second group was exposed to microgravity but did not imagine movements inflight. The third group was exposed to microgravity and imagined movements inflight. All groups performed and imagined the movements before and after the flight. We predicted that a mere exposure to microgravity would induce changes in imagined movement duration. We found this held true for the group who imagined the movements, suggesting an update of internal representations of gravity. However, we did not find a similar effect in the group exposed to microgravity despite the fact participants lived the same gravitational variations as the first group. Overall, these results suggest that motor imagery contributes to update internal representations of movement in unfamiliar environments, while a mere exposure proved to be insufficient.

A Study of the Classification of Motor Imagery Signals using Machine Learning Tools

In this paper, we propose a system for the purpose of classifying Electroencephalography (EEG) signals associated with imagined movement of right hand and relaxation state using machine learning algorithm namely Random Forest Algorithm. The EEG dataset used in this research was created by the University of Tubingen, Germany. EEG signals associated with the imagined movement of right hand and relaxation state were processed using wavelet transform analysis with Daubechies orthogonal wavelet as the mother wavelet. After the wavelet transform analysis, eight features were extracted. Subsequently, a feature selection method based on Random Forest Algorithm was employed giving us the best features out of the eight proposed features. The feature selection stage was followed by classification stage in which eight different models combining the different features based on their importance were constructed. The optimum classification performance of 85.41% was achieved with the Random Forest classifier. This research shows that this system of classification of motor movements can be used in a Brain Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.

F-Value Time-Frequency Analysis: Between-Within Variance Analysis

Studies on brain mechanisms enable us to treat various brain diseases and develop diverse technologies for daily life. Therefore, an analysis method of neural signals is critical, as it provides the basis for many brain studies. In many cases, researchers want to understand how neural signals change according to different conditions. However, it is challenging to find distinguishing characteristics, and doing so requires complex statistical analysis. In this study, we propose a novel analysis method, FTF (F-value time-frequency) analysis, that applies the F-value of ANOVA to time-frequency analysis. The proposed method shows the statistical differences among conditions in time and frequency. To evaluate the proposed method, electroencephalography (EEG) signals were analyzed using the proposed FTF method. The EEG signals were measured during imagined movement of the left hand, right hand, foot, and tongue. The analysis revealed the important characteristics which were different among different conditions and similar within the same condition. The FTF analysis method will be useful in various fields, as it allows researchers to analyze how frequency characteristics vary according to different conditions.

Stopping vs Resting state during motor imagery paradigm

Current non-invasive Brain Machine interfaces commonly rely on the decoding of sustained motor imagery activity (MI). This approach enables a user to control brain-actuated devices by triggering predetermined motor actions. One major drawback of such strategy is that users are not trained to stop their actions. Indeed, the termination process involved in BMI is poorly understood with most of the studies assuming that the end of an MI action is similar to the resting state. Here we hypothesize that the process of stopping MI (MI termination) and resting state are two different processes that should be decoded independently due to the exhibition of different neural pattens. We compared the detection of both states transitions of an imagined movement, i.e. rest-to-movement (onset) and movement-to-rest (offset). Our results shows that both decoders show significant differences in term of performances and latency (N=17 Subjects) with the offset decoder able to detect faster and better MI termination. While studying this difference, we found that the offset decoder is primarily based on the use of features in Beta band which appears earlier. Based on this finding, we also proposed a Random Forrest based decoder which enable to distinguish three classes (MI, MI termination and REST).

The human primary somatosensory cortex encodes imagined movement in the absence of sensory information

Classical systems neuroscience positions primary sensory areas as early feed-forward processing stations for refining incoming sensory information. This view may oversimplify their role given extensive bi-directional connectivity with multimodal cortical and subcortical regions. Here we show that single units in human primary somatosensory cortex encode imagined reaches in a cognitive motor task, but not other sensory–motor variables such as movement plans or imagined arm position. A population reference-frame analysis demonstrates coding relative to the cued starting hand location suggesting that imagined reaching movements are encoded relative to imagined limb position. These results imply a potential role for primary somatosensory cortex in cognitive imagery, engagement during motor production in the absence of sensation or expected sensation, and suggest that somatosensory cortex can provide control signals for future neural prosthetic systems.

Examining the equivalence between imagery and execution within the spatial domain – Does motor imagery account for signal-dependent noise?

Motor imagery is suggested to be functionally equivalent to physical execution as they each utilise a common neural representation. The present study examined whether motor imagery correspondingly reflects the spatial characteristics of physically executed movements, including the signal-dependent noise that typically manifests in more variable end locations (as indicated by effective target width; We). Participants executed or imagined a single, upper-limb target-directed aim in the horizontal medio-lateral direction. The start and end of the imagined movements were indexed by the lifting and lowering of the limb over the home position, respectively. Following each imagined movement, participants had to additionally estimate their imagined end location relative to the target. All the movements had to be completed at a pre-specified criterion time (400 ms, 600 ms, 800 ms). The results indicated that the We increased following a decrease in movement time for execution, but not imagery. Moreover, the total error of imagined movements was greater than the actual error of executed movements. While motor imagery may comprise a neural representation that also contributes to the execution of movements, it is unable to closely reflect the random sources of variability. This limitation of motor imagery may be attributed to the comparatively limited efferent motor signals.

Decay of inhibition in motor imagery

During motor imagery, global inhibition and effector-specific inhibition contribute to prevent actual movements. We investigated the decay of inhibition using an action-mode switching paradigm. Participants switched between imagined and executed hand movements. Response–stimulus intervals (RSIs) were varied (200, 700, 1,300, and 2,000 ms). As inhibition (due to imagination) or activation (due to execution) in one trial affects performance in the subsequent trial, we analysed sequential effects. Evidence for the contribution of global inhibition (e.g., switch benefits in execution [E]—imagination [I] sequences compared with I-I sequences) and effector-specific inhibition (e.g., hand repetition costs after an imagination trial) was observed. Sequential effects decreased with increasing RSIs, indicating that both forms of inhibition are subject to decay. However, the decrease of sequential effects was less pronounced for global inhibition than for effector-specific inhibition. This indicates that global inhibition may decay slowly, whereas effector-specific inhibition decays rather quickly. In conclusion, global inhibition may be at least partly implemented in all contexts in which motor imagery has to be performed, whereas effector-specific inhibition may contribute to motor imagery only as soon as the exact movement parameters are known and may decay quickly after the imagined movement has been performed.

Electromyography features during physical and imagined standing up in healthy young adults, Phitsanulok, Thailand

PurposeTo compare the electromyography (EMG) features during physical and imagined standing up in healthy young adults.Design/methodology/approachTwenty-two participants (ages ranged from 20–29 years old) were recruited to participate in this study. Electrodes were attached to the rectus femoris, biceps femoris, tibialis anterior and the medial gastrocnemius muscles of both sides to monitor the EMG features during physical and imagined standing up. The %maximal voluntary contraction (%MVC), onset and duration were calculated.FindingsThe onset and duration of each muscle of both sides had no statistically significant differences between physical and imagined standing up (p > 0.05). The %MVC of all four muscles during physical standing up was statistically significantly higher than during imagined standing up (p < 0.05) on both sides. Moreover, the tibialis anterior muscle of both sides showed a statistically significant contraction before the other muscles (p < 0.05) during physical and imagined standing up.Originality/valueMuscles can be activated during imagined movement, and the patterns of muscle activity during physical and imagined standing up were similar. Imagined movement may be used in rehabilitation as an alternative or additional technique combined with other techniques to enhance the STS skill.