Stroke is a leading cause of disability worldwide. In this paper, a novel robot-assisted rehabilitation system based on motor imagery electroencephalography (EEG) is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three-dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT) and linear discriminant analysis (LDA) classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot-assisted upper limb rehabilitation system, the patient's EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patient's motivation and attention and directly facilitate upper limb post-stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot-assisted training with motor imagery-based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.

It is of importance to image electrical activity and properties of biological tissues. Recently hybrid imaging modality combing ultrasound scanning and source imaging through the acousto-electric (AE) effect has generated considerable interest. Such modality has the potential to provide high spatial resolution current density imaging by utilizing the pressure induced AE resistivity change confined at the ultrasound focus. In this study, we investigate a novel 3-dimensional (3D) ultrasound current source density imaging (UCSDI) approach using unipolar ultrasound pulses. Utilizing specially designed unipolar ultrasound pulses and by combining AE signals associated to the local resistivity changes at the focusing point, we are able to reconstruct the 3D current density distribution with the boundary voltage measurements obtained while performing a 3D ultrasound scan. We have shown in computer simulation that using the present method, it is feasible to image with high spatial resolution an arbitrary 3D current density distribution in an inhomogeneous conductive media.

Parameter estimation of chaotic systems is an important problem in nonlinear science and has aroused increasing interest of many research fields, which can be basically reduced to a multidimensional optimization problem. In this paper, an improved boundary bird swarm algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the good global convergence and robustness of the bird swarm algorithm and the exploitation capability of improved boundary learning strategy. Experiments are conducted on the Lorenz system and the coupling motor system. Numerical simulation results reveal the effectiveness and with desirable performance of IBBSA for parameter estimation of chaotic systems.

Hybrid imaging modality combining ultrasound scanning and electrical current density imaging through the acoustoelectric (AE) effect may potentially provide solutions to imaging electrical activities and properties of biological tissues with high spatial resolution. In this study, a 3-D reconstruction solution to ultrasound current source density imaging (UCSDI) by means of Wiener deconvolution is proposed and evaluated through computer simulations. As compared to previous 2-D UCSDI problem, in a 3-D volume conductor with broadly distributed current density field, the AE signal becomes a 3-D convolution between the electric field and the acoustic field, and effective 3-D reconstruction algorithm has not been developed so far. In the proposed method, a 3-D ultrasound scanning is performed while the corresponding AE signals are collected from multiple electrode pairs attached on the surface of the imaging object. From the collected AE signals, the acoustic field and electric field were first decoupled by Wiener deconvolution. Then, the current density distribution was reconstructed by inverse projection. Our simulations using artificial current fields in homogeneous phantoms suggest that the proposed method is feasible and robust against noise. It is also shown that using the proposed method, it is feasible to reconstruct 3-D current density distribution in an inhomogeneous conductive medium.