Multi-band component analysis for EEG artifact removal and source reconstruction with application to gamma-band activity

Abstract

Independent component analysis (ICA) has been used extensively for artifact removal and reconstruction of neuronal time-courses in electroencephalography (EEG). Typically, ICA is applied on wide-band EEG (for example 1 to 100 Hz or similar ranges). EEG captures the activities of a large number of sources and the as the number of the components separated by the ICA is limited by the number of the sensors, only the stronger sources (in terms of magnitude and duration) will be detected by the ICA, and the activity of weaker sources will be lost or scattered amongst the stronger components. Because of the 1/f nature of the EEG spectra this biases components to the lower frequency ranges. Here we used multi-band ICA; a versatile combination of a filter bank, PCA, and ICA, to increase the number of the ICA components substantially and improve the SNR of the separated components. Using band-pass filtering we break the original signal mixture into several subbands, then using PCA we reduce the dimensionality of each subband, before applying ICA to a matrix containing all the principal components from each band. Using simulated sources and real EEG from participants, we demonstrate that multi-band ICA is able to outperform the traditional wide-band ICA in terms of both signal-to-noise ratio of the separated sources and the number of the identified independent components. We successfully separated the gamma-band neuronal components that were time-locked to a visual stimulus, as well as weak sources which are not detectable by wide-band ICA.