On the characterization of cognitive tasks using activity-specific short-lived synchronization between electroencephalography channels

Accurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and complex nature brain. The majority proposed approaches assume stationarity in disregard systematic timing organization among regions tasks. In this study, we propose novel recognition method that captures activity-specific parameters from training data elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated power parameter triplets motor imagery framework. consist latency maximally synchronized signal segments onset Δt, time lag τ, duration w. used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence linearized mutual information, cross-correntropy calculate channel synchronizations at specific parameters. Recognition performances as well statistical analyses on both BCI Competition-III dataset IVa PhysioNet Motor Movement/Imagery dataset, indicate inter-channel calculated using elicit significantly distinct profiles for different tasks can thus reliably be purposes.