“…Electroencephalography (EEG) is highly nonlinear and entropy measures have long been used in clinical practice to reveal the nonlinear nature, for example, in classifying walking limitations [ 1 ], analyzing complexity and variability of trunk accelerations in patients with Parkinson’s Disease [ 2 , 3 ], differentiating balance patterns in diabetic patients with and without neuropathy [ 4 ], assessing anesthetic drug effects on the brain [ 5 ], identifying fetal distress [ 6 ], autism spectrum disorder in children [ 7 ], tinnitus [ 8 ], attention deficit hyperactivity disorder [ 9 ], epilepsy [ 10 ], Alzheimer’s disease [ 11 ], schizotypy [ 12 ], mind wandering [ 13 ] and psychogenic non-epileptic seizures [ 14 ]. Examples of entropy measures are permutation entropy [ 15 ], approximate entropy [ 16 ], neural network entropy [ 17 ], dispersion entropy [ 18 ], sample entropy [ 19 , 20 ] and their variants [ 21 – 25 ].…”