Simulated Eyeblink Artifact Removal with ICA: Effect of Measurement Uncertainty

TL;DR

This study investigates how measurement uncertainty affects ICA-based eyeblink artifact removal in EEG signals, comparing FastICA and Infomax algorithms across various electrode setups and noise levels.

Contribution

It characterizes measurement uncertainty distribution in EEG ADCs and evaluates its impact on ICA artifact removal performance and execution time.

Findings

Measurement uncertainty follows a Gaussian distribution.

Performance degradation is less than 5% at SNR > 15 dB.

FastICA's execution time decreases with higher measurement uncertainty.

Abstract

Independent Component Analysis (ICA) is commonly-used in electroencephalogram (EEG) signal processing to remove non-cerebral artifacts from cerebral data. Despite the ubiquity of ICA, the effect of measurement uncertainty on the artifact removal process has not been thoroughly investigated. We first characterize the measurement uncertainty distribution of a common ADC and show that it quantitatively conforms to a Gaussian distribution. We then evaluate the effect of measurement uncertainty on the artifact identification process through several computer simulations. These computer simulations evaluate the performance of two different ICA algorithms, FastICA and Infomax, in removing eyeblink artifacts from five different electrode configurations with varying levels of measurement uncertainty. FastICA and Infomax show similar performance in identifying the eyeblink artifacts for a given uncertainty level and electrode configuration. We quantify the correlation performance degradation with respect to SNR and show that in general, an SNR of greater than 15 dB results in less than a 5% degradation in performance. The biggest difference in performance between the two algorithms is in their execution time. FastICA's execution time is dependent on the amount of measurement uncertainty, with a 50% to 85% reduction in execution time over an SNR range of 20 dB. This contrasts with Infomax's execution time, which is unaffected by measurement uncertainty.