Performance prediction of adaptive filters for EEG signal

Abstract

A methodology for predicting the performance of algorithm in terms of fidelity parameters is introduced. Algorithm performance is measured in terms of space and time complexities required to solve a particular problem. The proposed approach correlates the performance with input data and output results. Initial theory of performance model generation is based on polynomial regressing and curve fitting of least mean square (LMS) algorithm for adaptive filtering. Testing and validation of the generated model has been done through the coefficient of determination parameters calculated over interpolation and extrapolation results. Other measures like adjusted R 2, sum of square error and mean square error are also reported. Overall study is summarised in form of algorithm, and is tested with LMS and recursive LMS algorithm of adaptive filtering applied over electroencephalography/event related potentials (EEG/ERP) noise removal to build up relation between input signal-to-noise ratio (SNR) and output fidelity parameters (like output SNR and correlation (r)).