  How the brain processes information from external stimuli in order to
perceive the world and act on it is one of the greatest questions in
neuroscience. To address this question different time series analyzes
techniques have been employed to characterize the statistical properties of
brain signals during cognitive tasks. Typically response-specific processes are
addressed by comparing the time course of average event-related potentials in
different trials type. Here we analyze monkey Local Field Potentials data
during visual pattern discrimination called Go/No-Go task in the light of
information theory quantifiers. We show that the Bandt-Pompe symbolization
methodology to calculate entropy and complexity of data is a useful tool to
distinguish response-related differences between Go and No-Go trials. We
propose to use an asymmetry index to statistically validate trial type
differences. Moreover, by using the multi-scale approach and embedding time
delays to downsample the data we can estimate the important time scales in
which the relevant information is been processed.
