摘要Background:Physiological,pharmacological,and pathological alterations of consciousness provide critical windows into its neural substrates.Given the inherent complexity and multidimensionality of consciousness,defining quantitative,dynamic signatures of neural activity,and translating them into clinically applicable tools remains challenge.This study aimed to build an electroencephalography(EEG)-based methodological guideline for clinical consciousness assessment.Methods:EEG signals were systematically categorized across periodic and aperiodic activity,connectivity and network topology,spatiotemporal dynamics,self-organized criticality,and transcranial magnetic stimulation(TMS)-evoked responses.These biomarkers were mapped onto a conceptual framework of consciousness,comprising wakefulness and internal/external awareness,based on their validation across clinical conditions.The discriminative efficacy of various biomarkers was then evaluated across four independent datasets.Results:Integrated EEG features each captured distinct yet complementary dimensions of consciousness,supporting a unified neurophysiological architecture underlying diverse alterations of consciousness.Spectral power and peak frequency tracked the loss of consciousness during propofol anesthesia and sleep.Steeper aperiodic slopes,loss of frontoparietal connectivity,disrupted small-world organization,and reduced effective dimensionality were particularly effective in distinguishing minimally conscious state(MCS)from unresponsive wakefulness syndrome(UWS).Additionally,spatiotemporal patterns exhibited consciousness-specific alterations,with both pharmacological and pathological alterations influencing specific microstate dynamics.Conclusions:Synthesizing integrated neural dynamics and multidimensional consciousness,this guideline establishes both methodological and theoretical foundations for translating neurophysiological biomarkers into clinical applications.While this work advances both conceptual clarity and practical methodology,large-scale validation across expanded clinical cohorts,experimental models,and multimodal platforms is essential to fully establish causal linkages and translational utility.