A method for analyzing the complexity time course of event-related EEG data is presented. The percentage of EEG complexity during imagination of hand movement relative to that during reference period is calculated for quantifying ERD (event-related desynchronization). The method is applied to two sets of event-related EEG recorded from two subjects. The preliminary results suggest that the relative increase and decrease of event-related EEG complexity could be an indicator of ERD/ERS (event-related synchronization). Dynamic complexity measure provides another idea for analyzing event-related EEG.

An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points. With the multiscale feature of WT's, the QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. The relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated. By using this method, the detection rate of QRS complexes is above 99.8% for the MIT/BIH database and the P and T waves can also be detected, even with serious baseline drift and noise.

Objective This paper presents classif ications of mental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) network with optimal centers and widths for the Brain-Computer Interface (BCI) schemes. Methods Initial centers and widths of the network are selected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during training phase according to a regularized error function considering the influence of their changes to output values. Re sults The optimizing process improves the performance of RBF network, and its best cognition rate of three task pairs over four subjects achieves 87.0%. Moreover, this network runs fast due to the fewer hidden layer neurons. Conclusion The adaptive RBF network with optimal centers and widths has high recognition rate and runs fast. It may be a promising classif ier for on-line BCI scheme.

In this paper, a model for Sprague-Dawley (SD) rat focal ischemic cerebral injury is presented. Based on this experimental model, the electroencephalogram (EEG) from the ischemic region and from a normal region are collected during the first 30 min of ischemia. The EEG bispectrum analysis is carefully investigated by using the third-order recursion method. We found that some characteristics of the bispectrum are very sensitive to focal ischemic cerebral injury. The maximum magnitude and the weighted center of EEG bispectrum (WCOB) change according to the extent and the place of the injury region. The bispectrum analysis results have been verified by the heat shock protein (HSP) test. The study indicates that the EEG bispectrum analysis may be useful to distinguish the ischemic region from the normal one and to estimate the ischemic extent.