Independent component approach to the analysis of EEG and MEG recordings

TLDR

Multichannel EEG and MEG recordings generate large data sets, and independent component analysis (ICA) is an effective tool for extracting artifacts and analyzing stimulus‑evoked brain signals. The paper reviews recent ICA applications to EEG and MEG recordings. The authors use ICA to decompose EEG and MEG signals into independent components for artifact removal and neural signal analysis.

Abstract

Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field.