  Multiscale inherent fuzzy entropy is an objective measurement of
electroencephalography (EEG) complexity, reflecting the habituation of brain
systems. Entropy dynamics are generally believed to reflect the ability of the
brain to adapt to a visual stimulus environment. In this study, we explored
repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by
assessing multiscale inherent fuzzy entropy with relative measurements. We used
a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40
participants under the following three conditions: a resting state (closed-eyes
(CE) and open- eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation
with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG
relative complexity with increasing stimulus times in CE and OE conditions. The
occipital EEG relative complexity was significantly higher for the fifth SSVEP
than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG
relative complexity tended to be significantly higher in the OE condition
compared to that in the CE condition (FDR-adjusted p < 0.05). The results also
indicate that multiscale inherent fuzzy entropy is superior to other competing
multiscale-based entropy methods. In conclusion, EEG relative complexity
increases with stimulus times, a finding that reflects the strong habituation
of brain systems. These results suggest that multiscale inherent fuzzy entropy
is an EEG pattern with which brain complexity can be assessed using repetitive
SSVEP stimuli.
