Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy

Abstract

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

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

“…where µ M (x) : X → [0, 1] represents the degree of support for membership of the x ∈ X of IFS, and v M (x) : X → [0, 1] represents the degree of support for non-membership of the x ∈ X of IFS, with the condition that 0 ≤ µ M (x) + v M (x) ≤ 1. and the hesitancy function π M (x) of IFS reflecting the uncertainty of membership and non-membership is defined by…”

“…where M Y (x) : X → [0, 1] represents the degree of support for membership of the x ∈ X of PFS, and M N (x) : X → [0, 1] represents the degree of support for non-membership of the x ∈ X of PFS, with the condition that…”

“…With the development of fuzzy mathematics, medical diagnosis has addressed more and more attention from the research society of applied computer mathematics. In the diagnostic process of medical profession, improving the processing capacity of various uncertain and inconsistent information and achieving more accurate decision-making have become the major challenges in the development of medical diagnosis [1][2][3]. Up to now, the process of medical diagnosis is driven by various theoretical studies, such as fuzzy sets theory [4][5][6], intuitionistic fuzzy sets [7][8][9][10], interval-valued intuitionistic fuzzy sets [11][12][13], and quantum decision [14][15][16].…”

Entailment for intuitionistic fuzzy sets based on generalized belief structures

“…Dealing with uncertainty is an open issue and many tools are presented to address this issue 44–47 . Many math models such as network analysis, 48–51 risk and reliability analysis, 52–54 visible graph, 55 and fuzzy sets 56–60 . IFS is an extension of the classical fuzzy sets, which has been used in a wide scope of application 61–65 .…”

Tensor Decomposition for EEG Signals Retrieval

“…Given a time series recorded physiological data, all data samples were carried by a vector. The power spectrum analysis of the time series has often been applied for investigating physiological (e.g., EEG) oscillations by computational intelligence models [7][8][9][10][11][12][13][14] and associated healthcare applications [15][16][17][18][19][20]. Recently, multiple electrodes are often used to collect EEG data in the experiment.…”