Uma abordagem de informação simbólica para caracterizar diferenças relacionadas à resposta na atividade cortical durante uma tarefa Go/No-Go

Abstract

How the brain processes information from external stimuli to perceive and act upon the world is one of the biggest questions in neuroscience. To answer this question, different time series analysis techniques have been employed to characterize the statistical properties of brain signals during cognitive tasks. Usually, specific response processes are addressed by comparing the time course of the average event-related potentials in different types of tests. Here, we analyze data from monkey Local Field Potentials during visual pattern discrimination called the Go/No-Go task in light of quantifiers from information theory. We show that the Bandt-Pompe symbolization methodology for calculating the entropy and complexity of the data is a useful tool for distinguishing response-related differences between Go and No-Go trials. We propose to use an asymmetry index to statistically validate the differences between trial types. Furthermore, by using the multi-scale approach and incorporating time delays to reduce data sampling, we can estimate the important time scales at which relevant information is processed.