  Inherent fuzzy entropy is an objective measurement of electroencephalography
(EEG) complexity, reflecting the robustness of brain systems. In this study, we
present a novel application of multi-scale relative inherent fuzzy entropy
using repetitive steady-state visual evoked potentials (SSVEPs) to investigate
EEG complexity change between two migraine phases, i.e. inter-ictal (baseline)
and pre-ictal (before migraine attacks) phases. We used a wearable headband EEG
device with O1, Oz, O2 and Fpz electrodes to collect EEG signals from 80
participants (40 migraine patients and 40 healthy controls [HCs]) under the
following two conditions: during resting state and SSVEPs with five 15-Hz
photic stimuli. We found a significant enhancement in occipital EEG entropy
with increasing stimulus times in both HCs and patients in the inter-ictal
phase but a reverse trend in patients in the pre-ictal phase. In the 1st SSVEP
, occipital EEG entropy of the HCs was significantly higher than that of
patents in the pre-ictal phase (FDR-adjusted p < 0.05). Regarding the
transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients
in the pre-ictal phase exhibited significantly lower values than patients in
the inter-ictal phase (FDR-adjusted p < 0.05). Furthermore, in the
classification model, the AdaBoost ensemble learning showed an accuracy of 81%
and AUC of 0.87 for classifying inter-ictal and pre-ictal phases. In contrast,
there were no differences in EEG entropy among groups or sessions by using
other competing entropy models, including approximate entropy, sample entropy
and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy
offers novel applications in visual stimulus environments and may have the
potential to provide a pre-ictal alert to migraine patients.
