Complexity Analysis of EEG Signals Based on Fuzzy Entropy: Power Spectrum and Entropy Features Across Different Levels of Consciousness

This study aims to investigate the complexity features of EEG signals under different levels of consciousness and uses the fuzzy entropy algorithm to analyze the resting-state EEG data of healthy subjects and patients with consciousness disorders. By calculating the power spectrum and fuzzy entropy of EEG signals, the study reveals the trends of changes in frequency domain and complexity as the level of consciousness decreases. The experimental results show significant differences in the power spectrum and fuzzy entropy between healthy subjects and patients with consciousness disorders, particularly in terms of energy distribution in low-frequency bands and EEG complexity. The findings suggest that fuzzy entropy can effectively distinguish patients with different consciousness levels and has potential applications in clinical diagnosis of consciousness disorders. Although a relatively basic fuzzy entropy algorithm was used in this study, the research methodology provides important insights for future EEG-based assessments of consciousness disorders.