Measures of entropy and complexity in altered states of consciousness

• Published in: Cognitive neurodynamics Vol. 12; no. 1; pp. 73 - 84
• Main Authors: Mateos, D. M., Guevara Erra, R., Wennberg, R., Perez Velazquez, J. L.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2018; Springer Nature B.V
• Subjects: Artificial Intelligence; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain; Cognition; Cognitive ability; Cognitive Psychology; Complexity; Computer Science; Consciousness; Convulsions & seizures; Dynamic systems theory; EEG; Electroencephalography; Entropy; Epilepsy; Magnetoencephalography; Neurosciences; Patients; Permutations; Random variables; Research Article; Seizures; Sensorimotor system; Sleep; Sleep and wakefulness; Time series; Wakefulness; Canada; Sleep; Consciousness; EEG; Lempel–Ziv complexity; Permutation entropy; iEEG; MEG; Seizures
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-017-9459-8

Quantification of complexity in neurophysiological signals has been studied using different methods, especially those from information or dynamical system theory. These studies have revealed a dependence on different states of consciousness, and in particular that wakefulness is characterized by a greater complexity of brain signals, perhaps due to the necessity for the brain to handle varied sensorimotor information. Thus, these frameworks are very useful in attempts to quantify cognitive states. We set out to analyze different types of signals obtained from scalp electroencephalography (EEG), intracranial EEG and magnetoencephalography recording in subjects during different states of consciousness: resting wakefulness, different 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 complexity versus entropy graphs, 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 findings were 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.