A new method called Complexity via State-space Entropy Rate (CSER) estimates neural signal complexity with better temporal resolution and spectral decomposition than the standard Lempel-Ziv complexity (LZ) approach. CSER matches LZ in distinguishing conscious states but offers two key advantages: it can break complexity down by frequency bands, and it provides temporal resolution about 100 times finer. Using MEG, EEG, and ECoG data from humans and monkeys, CSER revealed that gamma-band activity primarily drives complexity changes across states of consciousness. In an auditory mismatch negativity experiment, CSER detected early entropy increases roughly 20 milliseconds before the standard event-related potential. This method enables finer-grained study of how signal complexity relates to cognitive processes and conscious states.
Neural complexity, measured by the Lempel-Ziv compression algorithm, is lowest during NREM sleep and similar during REM sleep and wakefulness in cats with intracranial electrodes. Under subanesthetic doses of ketamine (5, 10, and 15 mg/kg), complexity follows an inverted U-shaped curve in some electrodes, especially in prefrontal cortex, rising at low doses and falling as doses approach anesthetic levels. Variability in the ketamine dose-response across cats and cortices is larger than sleep-stage differences, revealing distinct local dynamics. These results replicate findings in humans and other species, showing neural complexity is sensitive to conscious state changes and dose-dependent ketamine effects.