Autism spectrum disorder (ASD) is a devastating mental disorder in children. Currently, there is no effective treatment for ASD. Transcranial direct current stimulation (tDCS), which is a non-invasive brain stimulation neuromodulation technology, is a promising method for the treatment of ASD. However, the manner in which tDCS changes the electrophysiological process in the brain is still unclear. In this study, we used tDCS to stimulate the dorsolateral prefrontal cortex area of children with ASD (one group received anode tDCS, and the other received sham tDCS) and investigated the changes in evoked EEG signals and behavioral abilities before and after anode and sham stimulations. In addition to tDCS, all patients received conventional rehabilitation treatment. Results show that although conventional treatment can effectively improve the behavioral ability of children with ASD, the use of anode tDCS with conventional rehabilitation can boost this improvement, thus leading to increased treatment efficacy. By analyzing the electroencephalography pre- and post-treatment, we noticed a decrease in the mismatch negativity (MMN) latency and an increase in the MMN amplitude in both groups, these features are considered similar to MMN features from healthy children. However, no statistical difference between the two groups was observed after 4 weeks of treatment. In addition, the MMN features correlate well with the aberrant behavior checklist (ABC) scale, particularly the amplitude of MMN, thus suggesting the feasibility of using MMN features to assess the behavioral ability of children with ASD.

The gait outcome measures used in clinical trials of paraplegic locomotor training determine the effectiveness of improved walking function assisted by the functional electrical stimulation (FES) system. Focused on kinematic, kinetic or physiological changes of paraplegic patients, traditional methods cannot quantify the walking stability or identify the unstable factors of gait in real time. Up until now, the published studies on dynamic gait stability for the effective use of FES have been limited. In this paper, the walker tipping index (WTI) was used to analyze and process gait stability in FES-assisted paraplegic walking. The main instrument was a specialized walker dynamometer system based on a multi-channel strain-gauge bridge network fixed on the frame of the walker. This system collected force information for the handle reaction vector between the patient's upper extremities and the walker during the walking process; the information was then converted into walker tipping index data, which is an evaluation indicator of the patient's walking stability. To demonstrate the potential usefulness of WTI in gait analysis, a preliminary clinical trial was conducted with seven paraplegic patients who were undergoing FES-assisted walking training and seven normal control subjects. The gait stability levels were quantified for these patients under different stimulation patterns and controls under normal walking with knee-immobilization through WTI analysis. The results showed that the walking stability in the FES-assisted paraplegic group was worse than that in the control subject group, with the primary concern being in the anterior-posterior plane. This new technique is practical for distinguishing useful gait information from the viewpoint of stability, and may be further applied in FES-assisted paraplegic walking rehabilitation.

Mental tasks such as motor imagery in synchronization with a cue which result event related desynchronization (ERD) and event related synchronization (ERS) are usually studied in brain-computer interface (BCI) system. In this paper we analyze and classify the ERD/ERS response evoked by the motor imagery of left hand, right hand, foot and tongue. The signals were spatially filtered by Independent Component Analysis (ICA) before calculating the power spectral density (PSD) for related electrodes, and then the Support Vector Machine (SVM) was adopted to recognise the different imagery pattern according to ERD/ERS feature for the signals. The results showed that the combination of ICA-based signal extraction algorithm and SVM-based classification method was an effective tool for the identification of motor imagery potentials, with the highest accuracy rate of 91.4% and 77.6% for the lowest.

Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

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