Identifying key factors for improving ICA‐based decomposition of EEG data in mobile and stationary experiments

TLDR

Recent advances in EEG hardware and analysis enable recordings in stationary and mobile settings, but noise contamination requires preprocessing, and ICA is a common tool for artifact removal and source analysis, with filtering being a key step to improve decomposition. The study aims to compare preprocessing requirements for ICA decomposition between mobile and stationary EEG experiments. The authors evaluated the effects of movement, channel count, and high‑pass filter cutoff on ICA decomposition. The study found that while standard settings (stationary, 64 channels, 0.5 Hz filter) yield acceptable ICA results, mobile experiments benefit from higher high‑pass cutoffs (up to 2 Hz) and more channels require higher filters, and the authors provide guidelines to optimize ICA decomposition across settings.

Abstract

Abstract Recent developments in EEG hardware and analyses approaches allow for recordings in both stationary and mobile settings. Irrespective of the experimental setting, EEG recordings are contaminated with noise that has to be removed before the data can be functionally interpreted. Independent component analysis (ICA) is a commonly used tool to remove artifacts such as eye movement, muscle activity, and external noise from the data and to analyze activity on the level of EEG effective brain sources. The effectiveness of filtering the data is one key preprocessing step to improve the decomposition that has been investigated previously. However, no study thus far compared the different requirements of mobile and stationary experiments regarding the preprocessing for ICA decomposition. We thus evaluated how movement in EEG experiments, the number of channels, and the high‐pass filter cutoff during preprocessing influence the ICA decomposition. We found that for commonly used settings (stationary experiment, 64 channels, 0.5 Hz filter), the ICA results are acceptable. However, high‐pass filters of up to 2 Hz cut‐off frequency should be used in mobile experiments, and more channels require a higher filter to reach an optimal decomposition. Fewer brain ICs were found in mobile experiments, but cleaning the data with ICA has been proved to be important and functional even with low‐density channel setups. Based on the results, we provide guidelines for different experimental settings that improve the ICA decomposition.