The cellular neural networks with delay (DCNN’s) are investigated, and some new sufficient conditions on asymptotical stability of DCNN’s are derived by constructing the Liapunov functional and utilizing M ? matrixand theω?limit set. It is shown that the new conditions are not related to the delayed parameter.
General linear model (GLM) is the most popular method for functional magnetic resource imaging (fMRI) data analysis . However, its theory is imperfect. The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises better. For the purpose of detecting brain function activation , according to the principle of GLM,a new convolution model is presented by a new dynamic function convolving with design-matrix,which combining with t-test can be used to detect brain active signal. The fMRI imaging result of visual stimulus experiment indicates that brain activities mainly concentrate among v1and v2 areas of visual cortex, and also verified the validity of this technique.
By the means of computing approximate entropy (ApEn) of video-EEG from some clinical epileptic, ApEn of EEG with epileptiform discharges is found significantly different from that of EEG without epileptiform discharges, (p=0.002). Meanwhile, dynamic ApEn shows consistent change of EEG signal with discharges of epileptic waves inside. These results suggest that ApEn may be a useful tool for automatic recognition and detection of epileptic activity and for understanding epileptogenic mechanism.
Human epilepsy is an intrinsic brain pathology, which can be characterized by repetitive high-amplitude electroencephalograph (EEG) activity. The wavelet transform provides an important tool in signal analysis and feature extraction. In this paper, the modulus maximum pair of a wavelet transform is used to detect the singularity value of the sharps and the spikes embedded in the background activities of the epilepsy EEG. The efficacy of the proposed method has been tested with clinical EEG.
The exponential stability of the delayed cellular neural networks （DCNN＇s） is investigated. By dividing the network state variables into some parts according to the characters of the neural networks, some new sufficient conditions of exponential stability are derived via constructing a Liapunov function. It is shown that the conditions differ from previous ones. The new conditions, which are associated with some initial value, are represented by some blocks of the interconnection matrix.
