Analysis of electrophysiological human intracranial data:
a symbolic information approach to characterize entropy and complexity during a cognitive visual search task
computational neuroscience, data analisis, complexity systems
Understanding how the brain processes information, perceives the world and creates models to act on it is one of the main goals of neuroscientists. In order to address this question, we can analyze cortical time series during different cognitive tasks to characterize the statistical properties of brain signals. Here we analyze electrocorticographic (ECoG) measurement of brain surface potentials in five human subjects during a visual search task in the light of information theory quantifiers. We employ the Bandt-Pompe symbolization methodology to calculate entropy and complexity for different time scales. We show that the different types of trials can be separated in the multi-scale entropy-complexity plan. It means that the information theory quantifiers are a useful tool to distinguish different trials. 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 being processed.