Measures of Entropy and Complexity in altered states of consciousness

Quantification of complexity in neurophysiological signals has been studied using different methods, especially those from information or dynamical system theory. These studies revealed the dependence on different states of consciousness, particularly that wakefulness is characterized by larger complexity of brain signals perhaps due to the necessity of the brain to handle varied sensorimotor information. Thus these frameworks are very useful in attempts at quantifying cognitive states. We set out to analyze different types of signals including scalp and intracerebral electroencephalography (EEG), and magnetoencephalography (MEG) in subjects during different states of consciousness: awake, sleep stages and epileptic seizures. The signals were analyzed using a statistical (Permutation Entropy) and a deterministic (Permutation Lempel Ziv Complexity) analytical method. The results are presented in a complexity vs entropy graph, showing that the values of entropy and complexity of the signals tend to be greatest when the subjects are in fully alert states, falling in states with loss of awareness or consciousness. These results are robust for all three types of recordings. We propose that the investigation of the structure of cognition using the frameworks of complexity will reveal mechanistic aspects of brain dynamics associated not only with altered states of consciousness but also with normal and pathological conditions.