Gaoxiang Ouyang

Detection of epileptic spikes with empirical mode decomposition and nonlinear energy operator

Epileptic seizure is a serious brain disease. The characteristic signature of epileptic seizure is interictal spikes and sharp waves. Development of a reliable method to detect spikes from EEG data is of major clinical and theoretical importance. In this paper, a new detection algorithm that combines the Empirical Mode Decomposition (EMD), Hilbert Transformation (HT) and Smoothed Nonlinear Energy Operator (SNEO) is proposed to detect spikes hidden in human EEG data. Finally, the EEG data generated by a nonlinear lumped-parameter cerebral cortex model and real EEG data from human are applied to test the performance of the new detection method. The results show that this method can efficiently detect the spikes hidden in EEG signals.

Automated Prediction of Epileptic Seizures in Rats with Recurrence Quantification Analysis

The prediction of epileptic seizures is a very important issue in the neural engineering. This is because it may improve the life quality of the patients who are suffering from uncontrolled epilepsy. In our earlier work, we found that the dynamical characteristics of EEG data with recurrence quantification analysis (RQA), also called complexity measure, can identify the differences among inter-ictal, pre-ictal and ictal phases. In this paper, we propose an automated technique with complexity measure of EEG recording to detect pre-ictal phase. Using the EEG recorded from rats with experimentally induced generalized epilepsy, it is found the method can detect the complexity changes of the neural activity prior to epileptic seizures. We suggest that the new method could be considered as an alternative of epileptic seizures prediction in practice

Ram position control in plastic injection molding machines with higher-order iterative learning

In plastic injection molding, the ram position plays an important role in production quality. This paper introduces a new method, which is a combination of the current cycle feedback control (a PI controller) and a feed-forward higher-order iterative learning control (ILC), to control the ram position in injection molding. The PI controller is used to stabilize the system, and the feed-forward higher-order ILC control is used to compensate for nonlinear/unknown dynamics and disturbances, thereby gaining the precision tracking to ram position. The simulation results indicate that the new method outperforms the conventional PI controller. In addition, it outperforms the conventional ILC in the convergence performance.

Ram Velocity Control in Plastic Injection Molding Machines with Neural Network Learning Control

In plastic injection molding, the ram velocity plays an important role in production quality. This paper introduces a new method, which is a combination of the current cycle feedback control and neural network (NN) learning, to control the ram velocity in injection process. It consists of two parts: a PD controller (current cycle feedback control) is used to stabilize the system, and the feedforward NN learning is used to compensate for nonlinear/unknown dynamics and disturbances, thereby enhancing the performance achievable with feedback control alone. The simulation results indicate that the proposed NN learning control scheme outperforms the conventional PD controller and can greatly reduce tracking errors as the iteration number increase.