A Linear Predictive Coding Filtering Method for the Time-resolved Morphology of EEG Activity

Abstract

This paper introduces a new time-resolved spectral analysis method based on the Linear Prediction Coding (LPC) method that is particularly suited to the study of the dynamics of EEG (Electroencephalography) activity. The spectral dynamics of EEG signals can be challenging to analyse as they contain multiple frequency components and are often corrupted by noise. The LPC Filtering (LPCF) method described here processes the LPC poles to generate a series of reduced-order filter transform functions which can accurately estimate the dominant frequencies. The LPCF method is a parameterized time-frequency method that is suitable for identifying the dominant frequencies of multiple-component signals (e.g. EEG signals). We define bias and the frequency resolution metrics to assess the ability of the LPCF method to estimate the frequencies. The experimental results show that the LPCF can reduce the bias of the LPC estimates in the low and high frequency bands and improved frequency resolution. Furthermore, the LPCF method is less sensitive to the filter order and has a higher tolerance of noise compared to the LPC method. Finally, we apply the LPCF method to a real EEG signal where it can identify the dominant frequency in each frequency band and significantly reduce the redundant estimates of the LPC method.