Premium
Identifying key factors for improving ICA‐based decomposition of EEG data in mobile and stationary experiments
Author(s) -
Klug Marius,
Gramann Klaus
Publication year - 2021
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/ejn.14992
Subject(s) - independent component analysis , preprocessor , computer science , noise (video) , electroencephalography , filter (signal processing) , channel (broadcasting) , artificial intelligence , pattern recognition (psychology) , data pre processing , speech recognition , computer vision , telecommunications , psychology , psychiatry , image (mathematics)
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.