Applying recurrence time entropy to identify changes in event-related potentials
Laboratory of Dynamics in Biological Systems, KU Leuven, 3000, Leuven, Belgium
2 Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, 236041, Kaliningrad, Russia
3 Neuroscience and Cognitive Technology Laboratory, Innopolis University, 420500, Innopolis, Russia
4 Institute of Information Technology, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, 603022, Nizhny Novgorod, Russia
Accepted: 29 November 2022
Published online: 9 December 2022
The event-related potentials (ERPs) are an essential response of the human brain to environmental changes that correlate with behavior. They are thus widely used as indicators of brain activity in fundamental research and response sources in brain communication devices. The problem of their robust identification from single-trial EEG recordings or limited data sets is timely and challenging. The current study addresses this issue by evaluating the ERP-associated variations of EEG signals using the measures of complexity based on the recurrence quantification analysis (RQA). Specifically, we demonstrate that the recurrence time entropy (RTE) is a good indicator of ERP-associated changes in the course of successive discrimination of ambiguous visual stimuli. Using the distribution of recurrence times, we conclude why exactly this measure is sensitive to ERP-associated variations of EEG signal.
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