Atlas of intracranial electroencephalography complexity across cortical regions and global states of consciousness in the human brain

Electroencephalography (EEG) signal complexity reflects the richness of brain activity and is considered a marker of consciousness. However, its normative values across physiological conscious states using intracranial EEG (iEEG) across cortical areas remain poorly defined. We aimed at referencing iEEG complexity in the human brain and its links with global states of consciousness. We analyzed 5,703 iEEG recordings from healthy cortices in 106 participants during wakefulness, N2, N3, and REM sleep, using complementary markers: Kolmogorov complexity, permutation entropy, and spectral entropy. An independent dataset comprising 474 recordings was used for validation and added data on epileptic cortices and propofol anesthesia. iEEG complexity decreased with reduced consciousness, with the highest values in wake/REM and the lowest under propofol. Complexity was more variable during N2/N3 sleep. Frontoparietal regions exhibited the highest complexity in conscious states. Epileptic cortex showed lower complexity than healthy cortex during wake and REM. A random forest classifier reliably classified sleep stages using complexity markers. Markers of iEEG signal complexity reliably index global states of consciousness. These normative data support the use of complexity as a marker of physiological consciousness, cortical integrity, and sleep-stage detection.