Evaluation of Denoising Techniques for EOG signals based on SNR Estimation

TL;DR

This study compares four denoising algorithms for raw EOG signals using SNR estimation via eigenvalues, assessing their effectiveness on data collected during a letter cancellation task with 20 subjects.

Contribution

It introduces a comparative evaluation of four distinct denoising techniques for EOG signals based on SNR, using a standardized dataset and eigenvalue-based SNR measurement.

Findings

FIR bandpass filters effectively reduce noise in EOG signals.

Wavelet transform provides superior noise suppression compared to other methods.

EMD and median hybrid filters show moderate denoising performance.

Abstract

This paper evaluates four algorithms for denoising raw Electrooculography (EOG) data based on the Signal to Noise Ratio (SNR). The SNR is computed using the eigenvalue method. The filtering algorithms are a) Finite Impulse Response (FIR) bandpass filters, b) Stationary Wavelet Transform, c) Empirical Mode Decomposition (EMD) d) FIR Median Hybrid Filters. An EOG dataset has been prepared where the subject is asked to perform letter cancelation test on 20 subjects.