A REVIEW OF NEURAL SIGNAL PROCESSING PARADIGMS BASED ON PHYSIOLOGICAL MODELS FOR EEG

- The electroencephalogram (EEG) is a non-invasive method of demonstrating cerebral function and it gives a course view of the neural activity. The EEG signal also indicates the electrical activity of the brain, which is highly random in nature. The EEG forward problem deals with the mapping of the current dipoles using the lead field matrix (LFM) of the observation model and finding the potential at an electrode. The hypothetical dipoles or a current distribution inside the head provides scalp potentials. However, the EEG inverse problem deals with the problem of estimating the spatially extended sources of the electroencephalogram from corresponding scalp recordings of the EEG, i.e. for estimating the current distribution within human brain. By introducing dynamical inverse solutions, it is possible to link systematically the temporal aspect of EEG time series modeling with the spatial aspect of the instantaneous inverse solutions. The EEG consists of an underlying background process with superimposed transient nonstationarities such as epileptic spikes (ESs). The detection of ESs in EEG is of particular importance in the diagnosis of epilepsy. The EEG signal is modeled as time-varying autoregressive (TVAR) model. The Kalman filter is used to estimate the parameters of the TVAR model. A threshold function is applied to estimate the EEG to detect epileptic spikes. The EEG is susceptible to various large signal contaminations or artifacts, like baseline wander, power line, muscle activity (EMG), Electrooc-culogram or eye blinking (EOG), electrocardiogram (ECG), electrode movement and the normal brain background activity (sharp alpha activity or SAA). An independent component analysis (ICA) method and cascade adaptive filter may be used efficiently to remove the artifacts and interferences.