Validating rationale of group-level component analysis based on estimating number of sources in EEG through model order selection

This study addresses how to validate the rationale of group component analysis (CA) for blind source separation through estimating the number of sources in each individual EEG dataset via model order selection. Control children, typically reading children with risk for reading disability (RD), and children with RD participated in the experiment. Passive oddball paradigm was used for eliciting mismatch negativity during EEG data collection. Data were cleaned by two digital filters with pass bands of 1–30 Hz and 1–15 Hz and a wavelet filter with the pass band narrower than 1–12 Hz. Three model order selection methods were used to estimate the number of sources in each filtered EEG dataset. Under the filter with the pass band of 1–30 Hz, the numbers of sources were very similar among different individual EEG datasets and the group ICA would be suggested; regarding the other two filters with much narrower pass bands, the numbers of sources were relatively diverse, and then, applying group ICA would not be appropriate. Hence, before group ICA is performed, its rationale can be logically validated by the estimated number of sources in EEG data through model order selection.

► Validating rationale of group component analysis (CA) to extract desired components of event-related potentials (ERPs) is critical, but largely ignored.
► Performing group CA on the temporally concatenated EEG datasets theoretically assumes the numbers of sources are invariable among individual datasets.
► Estimating the number of sources in individual EEG datasets via model order selection can logically test the rationale.