A symbolic information approach applied to human intracranial data to characterize and distinguish different congnitive processes

How the human brain processes information during different cognitive tasks is one of the greatest questions in contemporary neuroscience. Understanding the statistical properties of brain signals during specific activities is one promising way to address this question. Here we analyze freely available data from implanted electrocorticography (ECoG) in five human subjects during two different cognitive tasks in the light of information theory quantifiers ideas. We employ a symbolic information approach to determine the probability distribution function associated with the time series from different cortical areas. Then we utilize these probabilities to calculate the associated Shannon entropy and a statistical complexity measure based on the disequilibrium between the actual time series and one with a uniform probability distribution function. We show that an Euclidian distance in the complexity-entropy plane and an asymmetry index for complexity are useful for comparing the two conditions. We show that our method can distinguish visual search epochs from blank screen intervals in different electrodes and patients. By using a multi-scale approach and embedding time delays to downsample the data, we find important time scales in which the relevant information is being processed. We also determine cortical regions and time intervals along the 2-second-long trials that present more pronounced differences between the two cognitive tasks. Finally, we show that the method is useful to distinguish cognitive processes using brain activity on a trial-by-trial basis.