Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks - Padilla-Buritica, Jorge I.; Ferrandez-Vicente, Jose M.; Castaño, German A.; Acosta-Medina, Carlos D. (Frontiers in Neuroscience, 2020)
Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing, under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we develop a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experiment with two approaches for piecewise segmentation of EEG recordings: Fixed time-window at which the estimates of FC measures fulfill a given confidence level, and variable time-window segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we present the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we perform a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses