Objective Active rehabilitation based on motor imagery(MI)is believed to help in the reshaping of the sensorimotor cortex.This study aims to investigate the activation of the sensorimotor cortex under visual and auditory guidance and its correlation,providing neurophysiological basis for audiovisual-assisted MI.Methods Participants were instructed to imagine flexing and lifting the left arm during training,while watching a video or hearing a corresponding audio command,constituting visual or auditory guidance for the left arm movement.The changes in the rate of variations in EEG μ rhythm event-related desynchronization(ERD)band energy in the associated brain areas and the multivariate power spectrum correlation coefficient between channels in the sensorimotor cortex were analyzed to characterize brain activity and correlation.Results Experimental results comparing the characteristics of MI,MI guided by synchronous action observation,MI guided by synchronous action hearing,and MI guided by synchronous action hearing and observation guidance showed that ERD measurements were in the order of MI guided by synchronous action hearing and observation>MI guided by synchronous action observation>MI guided by synchronous action hearing>MI.The multivariate power spectrum correlation coefficient for C4 channel with O1,Oz,and O2 channels was highest in the MI guided by synchronous action hearing and observation group and second highest in the MI guided by synchronous action observation group,with no clear differences between the other two groups.For C4 channel with T7 and T8 channels,the MI guided by synchronous action hearing group had the highest correlation,followed by the MI guided by synchronous action hearing and observation group,with no significant differences between the other two groups.The O1,Oz,and O2 channels with T7 and T8 channels showed the highest correlation in the MI guided by synchronous action hearing and observation group,with no clear differences between the other three groups.Conclusion MI synchronized with visual and auditory stimulation of imagined actions can significantly enhance sensorimotor cortex activity and strengthen functional connectivity between cortical regions,especially with the addition of audiovisual guidance.

Objective To study the correlation between brain network topology and characteristic parameters of working memory(WM)and motor imagery(MI)under transcranial direct current stimulation(tDCS).Methods After the subjects were stimulated,the EEG signals(EEG)of the right hand hand movement(stretching arm)and finger movement(holding cup of water in hand)were imagined by observing the plane pictures,and the EEG under the WM load of the third and fourth pictures of the subjects were used for comparative analysis.The changes of EEG network characteristics of MI and WM tasks with different difficulty were analyzed and the correlation of the change rate of the same EEG network characteristic parameters of MI and WM tasks with similar difficulty was verified.Results Following anodal transcranial direct current stimulation(tDCS),the network characteristic parameters in both motor imagery(MI)tasks and memory load tasks significantly increased compared to sham tDCS stimulation(P?

Neuromuscular electrical stimulation (NMES) has been proven to promote human balance, but research on its impact on motor ability mainly focuses on external physical analysis, with little analysis on the intrinsic neural regulatory mechanisms. This study, for the first time, investigated the effects of NMES on cortical activity and cortico-muscular functional coupling (CMFC) during standing balance. Twelve healthy subjects were recruited in bilateral NMES training, with each session consisting of 60 electrically induced isometric contractions. Electroencephalogram (EEG) signals, electromyogram (EMG) signals, and center of pressure (COP) signals of the foot sole were collected before stimulation, two weeks after stimulation, and four weeks after stimulation while the subjects maintained standing balance. The results showed that NMES training improved subjects' postural stability during standing balance. Additionally, based on the EMG power spectral density (PSD), the κ frequency band was defined, and EEG-EMG time-frequency maximal information coefficients (TFMIC) were calculated. It was found that NMES enhanced functional connectivity between the cortex and lower limb muscles, with varying degrees of increase in β-κ and γ-κ frequency band CMFC after stimulation. Furthermore, sample entropy (SE) of EEG signals also increased after training. The results of this study confirm that NMES training can enhance CMFC and brain activation during standing balance. This study, from the perspective of physiological electrical signals, validates the effectiveness of NMES for balance training and provides objective assessment metrics for the training effects of NMES.
Humans ; Postural Balance/physiology* ; Electromyography ; Electroencephalography ; Muscle, Skeletal/physiology* ; Electric Stimulation ; Motor Cortex/physiology* ; Male ; Standing Position ; Adult ; Female

As is well known that the quality of training samples directly influence the recognizing ability of neural network. In this paper, we introduce a method for solving the problem of how to classify the pattern of forearm by obtaining typical samples. At first, the original samples were pretreated by using the membership class function that can improve the quality of cluster sample. Then, the center of clustering could be gained by using the method of clustering and the typical sample was obtained. Based on this method, we can get the typical sample that corresponds with the movements of stretch of arm and fold of arm. We can make them as the training sample of the BP network to classify the pattern of forearm. The experiment indicates that this measure can improve the point of identification.
Algorithms ; Cluster Analysis ; Electromyography ; methods ; Forearm ; physiology ; Humans ; Neural Networks (Computer) ; Pattern Recognition, Automated ; methods ; Signal Processing, Computer-Assisted

Objective To study a processing method for EEG signals mixed with EOG and ECG signals disturbance.Methods First,the EEG was denoised by the hard threshold method,the soft threshold method,the compromise threshold method and the ? law threshold method in the second generation wavelet,and then the denoised EEG which still contained EOG and ECG was separated by fast independent component analysis( FastICA) algorithm.Results The ? law threshold method of the second generation wavelet had better denoising effect and FastICA algorithm had more ideal separate performance.Conclusion It is an effective preprocessing method for EEG in denoising with the ? law threshold method of the second generation wavelet and then in separating disturbance of independent source with FastICA algorithm.