Abstract
Multi-subject or group-level component analysis provides a data-driven approach to study properties of brain networks. Algorithms for group-level data decomposition of functional magnetic resonance imaging data have been brought forward more than a decade ago and have significantly matured since. Similar applications for electroencephalographic data are at a comparatively early stage of development though, and their sensitivity to topographic variability of the electroencephalogram or loose time-locking of neuronal responses has not yet been assessed. This study investigates the performance of independent component analysis (ICA) and second order blind source identification (SOBI) for data decomposition, and their combination with either temporal or spatial concatenation of data sets, for multi-subject analyses of electroencephalographic data. Analyses of simulated sources with different spatial, frequency, and time-locking profiles, revealed that temporal concatenation of data sets with either ICA or SOBI served well to reconstruct sources with both strict and loose time-locking, whereas performance decreased in the presence of topographical variability. The opposite pattern was found with a spatial concatenation of subject-specific data sets. This study proofs that procedures for group-level decomposition of electroencephalographic data can be considered valid and promising approaches to infer the latent structure of multi-subject data sets. Yet, specific implementations need further adaptations to optimally address sources of inter-subject and inter-trial variance commonly found in EEG recordings.
Highlights
Recent years saw a rapid advance in the development of methods for the analysis of large multisubject data sets in neuroscience, not least because signals and images obtained from the brain are complex and hard to structure without reverting to computational methods (Lemm et al, 2011)
This paper sets out to evaluate applications of independent component analysis (ICA) and second order blind identification (SOBI) for the analysis of EEG group-level decomposition multi-subject EEG data, focusing on approaches that directly infer a structure of sources common to subjects and considering major sources of variance in EEG, namely temporal jittering of neuronal responses and inter-individual variability of scalp topographies
Lio and Boulinguez (2013), for example, showed that SOBI performs significantly better than Infomax ICA in context of group ICA for EEG, when mixing matrices vary slightly as it would result from differences in electrode placements
Summary
Recent years saw a rapid advance in the development of methods for the analysis of large multisubject data sets in neuroscience, not least because signals and images obtained from the brain are complex and hard to structure without reverting to computational methods (Lemm et al, 2011). A predominant goal of current developments is to directly make inferences on the general nature or structure of neurocognitive processes underlying a specific task context or disease state Many of these group-level analyses have been developed in the context of functional magnetic resonance imaging (fMRI, e.g., Calhoun and Adal, 2012), but more recent developments address other modalities such as electroencephalography (EEG). To be detected at an EEG electrode, currents generated at a specific brain region have to traverse through the different tissue types of the brain, as well as the scull and the scalp This process usually is referred to as volume conduction, which causes surface electrode recordings to reflect mixtures of the temporal profiles of concurrently active brain sources (Nunez et al, 1997; Winter et al, 2007).
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