TY - JOUR
T1 - Group-level component analyses of EEG
T2 - Validation and evaluation
AU - Huster, Rene J.
AU - Plis, Sergey M.
AU - Calhoun, Vince D.
N1 - Publisher Copyright:
© 2015 Huster, Plis and Calhoun.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Blind source separation
KW - EEG
KW - Group component analysis
KW - ICA
KW - Multisubject analysis
KW - SOBI
UR - http://www.scopus.com/inward/record.url?scp=84938533818&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84938533818&partnerID=8YFLogxK
U2 - 10.3389/fnins.2015.00254
DO - 10.3389/fnins.2015.00254
M3 - Article
C2 - 26283897
AN - SCOPUS:84938533818
SN - 1662-4548
VL - 9
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
IS - JUL
M1 - 00254
ER -