Group-level component analyses of EEG: validation and evaluation.
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.
Department of Psychology, University of Oslo Oslo, Norway ; The Mind Research Network Albuquerque, NM, USA.
Rene J
RJ
Huster
The Mind Research Network Albuquerque, NM, USA.
Sergey M
SM
Plis
The Mind Research Network Albuquerque, NM, USA ; Department of Computer Science, University of New Mexico Albuquerque, NM, USA ; Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA.
Vince D
VD
Calhoun
EEG; ICA; SOBI; blind source separation; group component analysis; multisubject analysis