Spectral validity and spindle detection of wearable frontal Electroencephalography: A per‐subject calibration framework and systematic validation against polysomnography using the Wearanize+ dataset

Background Wearable Electroencephalography (EEG) devices enable home sleep monitoring but require systematic spectral validation before their physiological outputs can serve as proxies for polysomnographic features. Aims This study provides comprehensive spectral validation of the Zmax EEG headband against concurrent polysomnography (PSG) using the Wearanize+ dataset. Materials and Methods Seventy‐one participants with adequate signal quality underwent simultaneous home PSG and Zmax recording. Bandpower correspondence, calibration robustness, within‐subject reliability, lateralisation and spindle detection were evaluated across all sleep stages. Absolute delta power values reported in this study were computed using 4‐s Fast Fourier Transform windows and should not be directly compared to normative values derived from 8‐s windows because the shorter window introduces a systematic positive offset in the delta band that does not cancel in absolute comparisons. Results Zmax systematically underestimates bandpower across all frequency bands (bias −0.41 to −0.74 log units), attributable to the active Fpz reference electrode. A per‐subject N2‐referenced calibration eliminates this bias; N2 calibration outperformed N3 and rapid eye movement alternatives (mean post‐calibration r = 0.601 vs. 0.479 and 0.489). Post‐calibration spectral correspondence was strong for alpha (N3: r = 0.806) and sigma (N3: r = 0.752). Within‐subject reliability was excellent (split‐half r > 0.99). Discussion Demographic factors explained <4% of offset variance. Lateralisation analysis was underpowered (36%–39% power; N = 194 required for 80% power). Spindle under‐detection was traced to YASA's relative sigma power pre‐filter; lowering this threshold recovered PSG‐equivalent counts with near‐zero bias. These findings establish a validated calibration framework and evidence‐based feature selection recommendations for Zmax‐based sleep biomarker research.