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Quantification of complexity in neurophysiological signals has been studiedusing different methods, especially those from information or dynamical systemtheory. These studies revealed the dependence on different states ofconsciousness, particularly that wakefulness is characterized by largercomplexity of brain signals perhaps due to the necessity of the brain to handlevaried sensorimotor information. Thus these frameworks are very useful inattempts at quantifying cognitive states. We set out to analyze different typesof signals including scalp and intracerebral electroencephalography (EEG), andmagnetoencephalography (MEG) in subjects during different states ofconsciousness: awake, sleep stages and epileptic seizures. The signals wereanalyzed using a statistical (Permutation Entropy) and a deterministic(Permutation Lempel Ziv Complexity) analytical method. The results arepresented in a complexity vs entropy graph, showing that the values of entropyand complexity of the signals tend to be greatest when the subjects are infully alert states, falling in states with loss of awareness or consciousness.These results are robust for all three types of recordings. We propose that theinvestigation of the structure of cognition using the frameworks of complexitywill reveal mechanistic aspects of brain dynamics associated not only withaltered states of consciousness but also with normal and pathologicalconditions.

Measures of Entropy and Complexity in altered states of consciousness