Supplementary file 1_Auditory paradigm-dependent EEG feature screening and representation for distinguishing consciousness states in male patients with disorders of consciousness.docx

EEG-based biomarkers offer a non-invasive approach for characterizing residual brain activity, yet their discriminative sensitivity may depend on auditory stimulus design.</p>Methods<p>EEG recordings were obtained from 15 male patients with DoC during four auditory stimulation paradigms.

Nonlinear, spectral power, and graph-theoretic features were extracted, yielding 136 candidate features. All individual features were independently evaluated using patient-level leave-one-out cross-validation (LOOCV) to characterize paradigm- and feature-domain-specific predictive performance. In a complementary exploratory optimization analysis, univariate statistical screening identified 26 features with nominal group differences (p < 0.05); these features were subsequently subjected to within-fold K-means clustering and principal component analysis (PCA), and compared with directly combined feature representations for MCS versus UWS/VS classification and CRS-R score regression.</p>Results<p>In the single-feature benchmark, the reversed-word sequence paradigm showed relatively higher averaged classification performance, while nonlinear features demonstrated comparatively stronger classification characteristics than spectral power and graph-theoretic features.