Neural Suppression Elicited During Motor Imagery Following the Observation of Biological Motion From Point-Light Walker Stimuli

Effects of handedness on brain oscillatory activity during imagery and execution of upper limb movements

Brain activation during left- and right-hand motor imagery is a popular feature used for brain-computer interfaces. However, most studies so far have only considered right-handed participants in their experiments. This study aimed to investigate how handedness influences brain activation during the processes of imagining and executing simple hand movements. EEG signals were recorded using 32 channels while participants repeatedly squeezed or imagined squeezing a ball using their left, right, or both hands. The data of 14 left-handed and 14 right-handed persons were analyzed with a focus on event-related desynchronization/synchronization patterns (ERD/S). Both handedness groups showed activation over sensorimotor areas; however, the right-handed group tended to display more bilateral patterns than the left-handed group, which is in contrast to earlier research results. Furthermore, a stronger activation during motor imagery than during motor execution could be found in both groups.

Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification

Exploring the effective signal features of electroencephalogram (EEG) signals is an important issue in the research of brain-computer interface (BCI), and the results can reveal the motor intentions that trigger electrical changes in the brain, which has broad research prospects for feature extraction from EEG data. In contrast to previous EEG decoding methods that are based solely on a convolutional neural network, the traditional convolutional classification algorithm is optimized by combining a transformer mechanism with a constructed end-to-end EEG signal decoding algorithm based on swarm intelligence theory and virtual adversarial training. The use of a self-attention mechanism is studied to expand the receptive field of EEG signals to global dependence and train the neural network by optimizing the global parameters in the model. The proposed model is evaluated on a real-world public dataset and achieves the highest average accuracy of 63.56% in cross-subject experiments, which is significantly higher than that found for recently published algorithms. Additionally, good performance is achieved in decoding motor intentions. The experimental results show that the proposed classification framework promotes the global connection and optimization of EEG signals, which can be further applied to other BCI tasks.

Effects of the social context on the neurophysiological correlates of observed error monitoring

Monitoring the motor performance of others, including the correctness of their actions, is crucial for the human behavior. However, while performance (and error) monitoring of the own actions has been studied extensively at the neurophysiological level, the corresponding studies on monitoring of others' errors are scarce, especially for ecological actions. Moreover, the role of the context of the observed action has not been sufficiently explored. To fill this gap, the present study investigated electroencephalographic (EEG) indices of error monitoring during observation of images of interrupted reach-to-grasp actions in social (an object held in another person's hand) and non-social (an object placed on a table) contexts. Analysis in time- and time-frequency domain showed that, at the level of conscious error awareness, there were no effects of the social context (observed error positivity was present for erroneous actions in both contexts). However, the effects of the context were present at the level of hand image processing: observing erroneous actions in the non-social context was related to larger occipito-temporal N1 and theta activity, while in the social context this pattern was reversed, i.e., larger N1 and theta activity were present for the correct actions. These results suggest that, in case of easily predictable ecological actions, action correctness is processed as early as at the level of hand image perception, since the hand posture conveys information about the action (e.g., motor intention). The social context of actions might make the correct actions more salient, possibly through the saliency of the correctly achieved common goal.

Activation Patterns of Functional Brain Network in Response to Action Observation-Induced and Non-Induced Motor Imagery of Swallowing: A Pilot Study

Action observation (AO) combined with motor imagery (MI) was verified as more effective in improving limb function than AO or MI alone, while the underlying mechanism of swallowing was ambiguous. The study aimed at exploring the efficacy of AO combined with MI in swallowing. In this study, twelve subjects performed the motor imagery of swallowing (MI-SW) during magnetoencephalography (MEG) scanning, and trials were divided into three groups: the non-induced group (control group, CG), male AO-induced group (M-AIG), and female AO-induced group (F-AIG). We used event-related spectral perturbations (ERSPs) and phase locking value (PLV) to assess the degree of activation and connectivity of the brain regions during MI-SW in the three groups. The results showed that compared to CG, F-AIG and M-AIG significantly activated more brain regions in the frontoparietal, attention, visual, and cinguloopercular systems. In addition, M-AIG significantly activated the sensorimotor cortex compared to CG and F-AIG. For the brain network, F-AIG and M-AIG increased the diffusion of non-hub hot spots and cold hubs to the bilateral hemispheres which enhanced interhemispheric functional connectivity and information transmission efficiency in the MI-SW task. This study provided supporting evidence that AO induction could enhance the effect of MI-SW and supported the application of AO-induced MI-SW in clinical rehabilitation.