Human brain state classification via permutation entropy of EEG phase dynamics across consciousness levels and inattentive-type ADHD
Athokpam Langlen Chanu, Youngjai Park, Jaesung Choi et al.
Permutation entropy derived from EEG phase dynamics distinguishes conscious from unconscious brain states in a general anesthesia dataset, and eyes-open from eyes-closed resting-state conditions, but does not reliably separate control subjects from individuals with inattentive-type ADHD. The analysis used ordinal patterns of EEG signals to quantify disorder in anterior–posterior information flow. Conscious, inattentive-type ADHD, and eyes-closed conditions showed lower mean values and larger standard deviations of permutation entropy. Classification models confirmed the separability of conscious/unconscious states and eyes-open/eyes-closed conditions, but not ADHD versus control groups, suggesting that information beyond ordinal patterns may be needed for detecting inattentive-type ADHD.