MethodsX. 2026 Jul 28;17:104077. doi: 10.1016/j.mex.2026.104077. eCollection 2026 Dec.
ABSTRACT
Wearable EEG-based Lie and Truth detection requires structured descriptor engineering to achieve reproducible results and ensure method transparency. The existing approaches use loosely integrated features alongside hidden modeling methods, which create challenges for understanding their results and verifying research progress across different studies. The existing research gap requires the development of a deterministic descriptor framework to document wearable EEG systems. The research presents the Structured Multi-Domain EEG Descriptor with Phase-Based Connectivity as a reproducible framework that uses five-channel wearable EEG signals to classify binary Lie-Truth. The method emphasizes coordinated descriptor organization and controlled evaluation, summarized as follows:•The structured integration of temporal statistics, fractal complexity, spectral power distributions, and phase-based connectivity within a unified descriptor representation augmented by meta-correlation modelling.•The processing framework provides detailed specifications for preprocessing, window-level segmentation, descriptor extraction, fold-wise normalization, classifier evaluation, and computational profiling.•A controlled evaluation strategy for examining statistical consistency, descriptor separability, domain-level feature contribution, and workstation-based computational feasibility.Validation on the publicly available LieWaves dataset demonstrates stable fold-wise behaviour and consistent classifier responses under controlled conditions.
PMID:42571525 | PMC:PMC13452325 | DOI:10.1016/j.mex.2026.104077
