Next generation prosthetics will rely massively on myoelectric "Pattern Recognition" (PR) based control approaches, to improve their users' dexterity. One major identified factor of successful functioning of these approaches lies in the training of amputees and in their understanding of how those prosthetics works. We thus propose here an intuitive pattern similarity biofeedback which can be easily used to train amputees and allow them to optimize their muscular contractions to improve their control performance. Experiments were conducted on twenty able-bodied participants and one transradial amputee. Their performance in controlling an interface through a myoelectric PR algorithm was evaluated; before and after a short automatic user training session consisting in using the proposed visual biofeedback for ten participants, and using a generic PR algorithm output feedback for the others ten. Participants who were trained with the proposed biofeedback increased their classification score for the retrained gesture (by 39.4%), without affecting the overall classification performance (which progressed by 10.2%) through over-training and increase of False Positive rate as observed in the control group. Additional analysis indicates a clear change in contraction strategy only in the group who used the proposed biofeedback. These preliminary results highlight the potential of this method which does not focus so much on over-optimizing the pattern recognition algorithm or on physically training the users, but on providing them simple and intuitive information to adapt or change their motor strategies to solve some misclassification issues.The objective of this study is to explore the diagnostic decision and sensitivity of the surface electromyogram (EMG) clustering index (CI) with respect to post-stroke motor unit (MU) alterations through a simulation approach by the existing motor neuron pool model and surface EMG model. In the simulation analysis, three patterns of diagnostic decisions were presented in 24 groups representing eight types in three degrees of MU alterations. Specifically, the CI decision exhibited an abnormally increased pattern for five types, an abnormally decreased pattern for two types, and an invariant pattern for one type. Furthermore, the CI diagnostic decision was found to be highly sensitive to three types because a 50% degree of alteration in these types resulted in a distinct deviation of 2.5 in the CI Z-score. The mixed CI patterns were confirmed in experimental data collected from the paretic muscles of 14 subjects with stroke, as compared to the healthy muscles of 10 control subjects. Given the simulation results as a guideline, the CI diagnostic decision could be interpreted from general neural or muscular changes into specific MU changes (in eight types). This can further promote clinical applications of the convenient surface EMG tool in examining and monitoring paretic muscle changes toward customized stroke rehabilitation.Much attention has been dedicated to clinical research of focal epilepsy, but the ability to derive a successful seizure control strategy based on unique dynamical features of the electroencephalogram is an unsolved problem. In this work, we introduce a basic model of spontaneous seizure dynamics and construct from it to a network model of focal-onset seizure dynamics. The full model is composed of coupled oscillators with scale-free network connectivity and a common slow variable. We find that global parameter changes and variation of the connectivity can drive the model from a quiescent state to recurrent seizures, and, eventually, to a permanent-seizure-state. Based on network synchronization features we design a stimulation scheme for the control of the fraction of nodes with strongest phase locking is proposed. Simulations lead to the identification of optimal stimuli for a given type of dynamics. Our results contribute to the development of a rational strategy for the non-surgical treatment of drug-resistant epilepsy.Ischemic damage after stroke disrupts the complex balance of inhibitory and excitatory activity within cortical network causing brain functional asymmetry. Cerebellar deep nuclei with its extensive projections to cortical regions could be a prospective target for stimulation to restore inter-hemispheric balance and enhance neural plasticity after stroke. In our study, we repeatedly stimulated the lateral cerebellar nucleus (LCN) by low-intensity focused ultrasound (LIFU) for 3 days to enhance rehabilitation after middle cerebral artery occlusion (MCAO) in a mouse stroke model. The neural activity of the mice sensorimotor cortex was measured using epidural electrodes and analyzed with quantified electroencephalography (qEEG). Pairwise derived Brain Symmetry Index (pdBSI) and delta power were used to assess the neurorehabilitative effect of LIFU stimulation. see more Compared to the Stroke (non-treated) group, the LIFU group exhibited a decrease in cortical pathological delta activity, significant recovery in pdBSI and enhanced performance on the balance beam walking test. These results suggest that cerebellar LIFU stimulation could be a non-invasive method for stroke rehabilitation through the restoration of interhemispheric balance.Motor imagery based brain-computer interface (MI-BCI) has been studied for improvement of patients' motor function in neurorehabilitation and motor assistance. However, the difficulties in performing imagery tasks limit its application. To overcome the limitation, an enhanced MI-BCI based on functional electrical stimulation (FES) and virtual reality (VR) is proposed in this study. On one hand, the FES is used to stimulate the subjects' lower limbs before their imagination to make them experience the muscles' contraction and improve their attention on the lower limbs, by which it is supposed that the subjects' motor imagery (MI) abilities can be enhanced. On the other hand, a ball-kicking movement scenario from the first-person perspective is designed to provide visual guidance for performing MI tasks. The combination of FES and VR can be used to reduce the difficulties in performing MI tasks and improve classification accuracy. Finally, the comparison experiments were conducted on twelve healthy subjects to validate the performance of the enhanced MI-BCI.