Stability of ICA decomposition across within-subject EEG datasets.
Independent Component Analysis (ICA) has been successfully used to identify brain related signals and artifacts from multi-channel electroencephalographic (EEG) data. However the stability of ICA decompositions across sessions from a single subject has not been investigated. The goal of this study was to isolate EEG independent components (ICs) across sessions for each subject so as to assess whether ICs are reproducible across sessions. We used 64-channel EEG data recorded from two subjects during a simple mind-wandering experiment. Each subject participated in 11 twenty-minute sessions over a period of five weeks. Extended Infomax ICA decomposition was performed on the continuous data of each session. We used a simple IC clustering technique based on correlation of scalp topographies. Several clusters of homogenous components were identified for each subject. Typical component clusters accounting for eye movement and eye blink artifacts were identified. Both clusters included one component from each recording session. In addition, several clusters corresponding to brain electrical sources, among them clusters exhibiting prominent alpha, beta and Mu band activities, included components from most sessions. These results present evidence that ICA can provide relatively stable solutions across sessions, with important implications for Brain Computer Interface research.
Brain and Cognition Research Center, Paul Sabatier University, Toulouse, France. romain.grandchamp@cerco.ups-tlse.fr
Romain
R
Grandchamp
Claire
C
Braboszcz
Scott
S
Makeig
Arnaud
A
Delorme
D000465:Algorithms; D016477:Artifacts; D001767:Blinking; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D016000:Cluster Analysis; D004569:Electroencephalography / Q000379:methods*; D005133:Eye Movements; D006801:Humans; D015233:Models, Statistical; D012535:Scalp / Q000473:pathology; D012815:Signal Processing, Computer-Assisted; D012984:Software