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 have revealed a dependence on different states of consciousness, and in particular that wakefulness is characterized by a g...

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Bibliographic Details
Published in:Cognitive neurodynamics 2018-02, Vol.12 (1), p.73-84
Main Authors: Mateos, D. M., Guevara Erra, R., Wennberg, R., Perez Velazquez, J. L.
Format: Article
Language:English
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
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Summary: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.
ISSN:1871-4080
1871-4099
DOI:10.1007/s11571-017-9459-8