Research on the Effect of MT+FES Training on Sensorimotor Cortex.

Purpose: Aiming at the motor recovery of patients with unilateral upper limb motor dysfunction after stroke, we propose a mirror therapy (MT) training method, which uses surface electromyography (sEMG) to identify movements on one side and control the other side to perform functional electrical stimulation (FES) while mirror therapy is used. And we verify the effect of this training method by analyzing the activity changes of the sensorimotor cortex. Method: Ten subjects (6 men and 4 women) were randomly divided into two groups according to 3 men and 2 women in each group: the experimental group (n = 5) received FES+MT training, and the control group (n = 5) received MT training. Both groups were trained at a fixed time at 9 : 00 am every day, each time lasting 20 minutes, once a day, 5 days a week, continuous training for 4 weeks, and the training action was elbow flexion training. During the training of the elbow flexion exercise, the experimental group applied FES with a frequency of 30 Hz, a pulse width of 100 µs, and a current of 10 mA to the muscles corresponding to the elbow flexion exercise, and rested for 10 s after 10-s stimulation. We collect the EEG of the elbow flexion motor imagery of all subjects before and after training, and calculate the eigenvalue E, and analyze the effect of FES+MT training on the activity of the cerebral sensorimotor cortex. Results: After repeated measure (RM) two-way ANOVA of the two groups, comparing the subjects' µ rhythm elbow flexion motor imagery eigenvalue E, the experimental group (after training) > the control group (after training) > before training. Conclusion: The FES+MT training method has obvious activation effect on the cerebral sensorimotor cortex.

Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network.

Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels. In addition, a weighted BFN is constructed and expressed by the sum of the existing edge weights to characterize the cerebral cortex activation degree in different movement patterns. Then, on the basis of the topological structures of seven mental tasks, three regional networks centered on the C3, C4, and Cz channels are constructed, which are consistent with correspondence between limb movement patterns and cerebral cortex in neurophysiology. Furthermore, the CIR of each regional functional network is calculated to form three-dimensional vectors. Finally, we use the support vector machine to learn a classifier for multiclass MI tasks. Experimental results show a significant improvement and demonstrate the success of the extracted feature CIR in dealing with MI classification. Specifically, the average classification performance reaches 88.67% which is higher than other competing methods, indicating that the extracted CIR is effective for MI classification.

Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.

Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abundant in real applications. Although the semi-supervised learning (SSL) allows us to utilize both labeled and unlabeled data to improve the classification performance as against supervised algorithms, it has been reported that unlabeled data occasionally undermine the performance of SSL in some cases. To overcome this challenge, we propose a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. Specifically, the ELM model is firstly used to predict unlabeled samples and then the collaborative representation (CR) approach is employed to reconstruct the unlabeled samples according to the obtained prediction results, from which the risk degree of unlabeled sample is defined. A risk-based regularization term is then constructed accordingly and embedded into the objective function of the SS-ELM. Experiments conducted on benchmark and EEG datasets demonstrate that the proposed method outperforms the ELM and SS-ELM algorithm. Moreover, the proposed CR-SSELM even offers the best performance while SS-ELM yields worse performance compared with its supervised counterpart (ELM). Graphical abstract This paper proposes a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. It is aim to solve the safety problem of SS-ELM method that SS-ELM yields worse performance than ELM. With the help of safety mechanism, the performance of our method is still better than supervised ELM method.

Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification.

Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs. While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency bands from all channels in the same scale as the intrinsic mode functions (IMFs), identifying and extracting the specific IMFs that contain significant information remain difficult. In this paper, a novel method has been developed to identify the information-bearing components in a low-dimensional subspace without prior knowledge. Our method trains a Gaussian mixture model (GMM) of the composite data, which is comprised of the IMFs from both the original signal and noise, by employing kernel spectral regression to reduce the dimension of the composite data. The informative IMFs are then discriminated using a GMM clustering algorithm, the common spatial pattern (CSP) approach is exploited to extract the task-related features from the reconstructed signals, and a support vector machine (SVM) is applied to the extracted features to recognize the classes of EEG signals during different motor imagery tasks. The effectiveness of the proposed method has been verified by both computer simulations and motor imagery EEG datasets.

Improving motor imagery practice with synchronous action observation in stroke patients.

BACKGROUND: Action observation (AO) has the potential to improve motor imagery (MI) practice in stroke patients. However, currently only a few results are available on how to use AO effectively. OBJECTIVE: The aim of this study is to investigate whether MI practice can be improved more effectively by synchronous AO than by asynchronous AO. METHODS: Ten patients with upper limb motor dysfunction following stroke were selected as the participants. They were divided into two groups to perform MI practice combined with a daily conventional rehabilitation for four consecutive weeks. The control group was asked to perform MI guided by asynchronous AO (MIAAO), and the experimental group was asked to perform the same MI but guided by synchronous AO (MISAO). The event-related power decrease (ERD) in sensorimotor rhythms of electroencephalograph was calculated to reflect the sensorimotor cortex activation and to assess the cortex excitability during MI. Fugl-Meyer assessment (FMA) and pinch strength test (PST) were used to assess the limb motor recovery. RESULTS: The ERD pattern of the experimental group not only had greater amplitude and longer duration, but also included more frequency components. Furthermore, the effect sizes of ERD values between the two groups continuously increased (dES > 0.8) during the course of treatment. Moreover, the FMA and PST scores achieved with MISAO were also significantly higher than those achieved with MIAAO (p < 0.05). CONCLUSIONS: Compared with MIAAO, MISAO can enhance the excitation of sensorimotor cortex more effectively and lead to a more rapid neurorehabilitation of stroke patients.