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.
A theoretical paper introduces new concepts—closure, compositionality, biobranes, and autobranes—to model consciousness in a non-reductionist way. It argues that consciousness co-arises with the non-trivial composition of biological closure, where conscious processes generate closed activity at various levels and are supported by biobranes and autobranes. This approach aims to integrate biological and phenomenological perspectives, offering a new framework for a science of consciousness. Future work will develop experimental definitions and computational simulations to characterize these dynamical biobranes.