noise estimation in eeg signal by using volterra series coefficients

Parameter estimation using Volterra series

A polynomial approximation to the likelihood function allows for marginalised estimates of model parameters to be obtained in the form of a Volterra series. The series can be applied directly to the observed data vector in an iterative fashion, to converge upon a set of parameter MAP estimates with low computational cost. A sample application towards OCR is used as an illustration.

Nonlinear prediction of speech signal using volterra-wiener series

Linear Prediction (LP) analysis has proven to be very effective and successful in speech analysis and speech synthesis applications. This may be due to the fact that LP analysis captures implicitly the time-varying vocal tract area function. However, it captures only the second-order statistical relationships and only the linear dependencies in the sequence of samples of speech signals (and not...

A New Solution to Volterra Series Estimation

Volterra series expansions represent an important model for the representation, analysis and synthesis of nonlinear dynamical systems. However, a significant problem with this approach to system identification is that the number of terms required to be estimated grows exponentially with the order of the expansion. In practice, therefore, the Volterra series is typically truncated to consist of,...

Efficient Estimation of Regression Coefficients in Time Series By

P81: Detection of Epileptic Seizures Using EEG Signal Processing

Epilepsy is the most common brain diseases that cause many problems in the daily life of the patient. In most attempts to automatic detection, the attack used an EEG. In this paper, The complete data set consists of five sets recorded from normal and epileptic patients. Each set containing 100 single-channel EEG segments. Here we used first and last sets (A and E). Set A consisted of segments r...

Fractal Dimension Analysis of EEG Time Series Signal Buried in Random Noise

The presence of correlated randomness in EEG time series signal buried in random noise was analyzed with the Higuchi fractal algorithm. The signal recognition algorithm design was built on the fact that the theoretical fractal dimension value of 2 is an indicator for a white noise time series and correlated randomness would alter the fractal dimension value from 2. However, the limited number o...