Brain signals are most complex when people are fully awake and alert, and complexity decreases during sleep and epileptic seizures. Researchers analyzed electroencephalography (EEG), intracranial EEG, and magnetoencephalography recordings from subjects during resting wakefulness, different sleep stages, and seizures. They used permutation entropy and permutation Lempel-Ziv complexity to measure signal complexity. Complexity-versus-entropy graphs showed that both measures were highest during wakefulness and fell during states with reduced awareness. These patterns held across all three recording types. The authors suggest that studying the structure of cognition through complexity frameworks can reveal brain dynamics underlying normal, altered, and pathological states of consciousness.
Wakefulness is characterized by greater complexity of brain signals compared to sleep or epileptic seizures. Scalp and intracerebral EEG and MEG recordings were analyzed using Permutation Entropy and Permutation Lempel Ziv Complexity. A complexity vs entropy graph showed that entropy and complexity values are highest during fully alert states and fall during states with loss of awareness or consciousness. These results were robust across all three recording types. The authors suggest that investigating cognition through complexity frameworks may reveal mechanistic aspects of brain dynamics in altered states of consciousness as well as normal and pathological conditions.