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A study of the propagation of electroencephalogram (EEG) activity before seizure by means of the Directed Transfer Function (DTF) is presented. The DTF method is a multi-channel parametric method of analysis based on an autoregressive model, and is capable of supplying such information as the direction, spectra and dynamics of the propagation of EEG signals. This method is typically utilized to determine...
Brain computer interface (BCI) is based on processing brain signals recorded from the scalp or the surface of the cortex in order to identify the different brain states and covert to corresponded control command. The key problems in BCI research are feature extraction and classification. In this paper, two experiments were performed, and the EEG data were recording during each experiment. One experiment...
Estimating head tissue conductivity for each layer is a high dimensional, non-linear and ill-posed problem which is part of Electrical Impedance Tomography (EIT) inverse problem. Traditional methods have many difficulties in resolving this problem. Support Vector Machine (SVM) based on Statistical Learning Theory (SLT) is a new kind of learning method including Support Vector Classification (SVC)...
Estimating head tissue conductivity for each layer is a high dimensional, non-linear and ill-posed problem which is part of Electrical Impedance Tomography (EIT) inverse problem. Traditional methods have many difficulties in resolving this problem. Support Vector Machine (SVM) based on Statistical Learning Theory (SLT) is a new kind of learning method including Support Vector Classification (SVC)...
The abnormal oscillations propagation in an actual neural tissue is often irregular and complex so that it can't be understood thoroughly until now, however some special work can prove helpful. In our work, a 2-D network is built where the nonlinear oscillators are coupled via resistors. Based on the network, a 2-D spatial-temporal partial differential equation (PDE) is presented to investigate the...
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