Brain-State-Dependent Biomarker Discovery from Multi-Condition EEG: A Structured Signal Framework for Schizophrenia with Interpretable Graph Neural Analysis

Suryansh Malhotra, Khyati Gupta, Neeru Rathee, Ram Pratap Beniwal, jyoti yadav

Keywords: electroencephalography, graph neural networks, schizophrenia, physiological time series, digital biomarkers, state-dependent biomarkers, phase-locking value, region-of-interest, multi-condition EEG

TL;DR: EEG biomarkers depend on brain state. Music beats resting-state EEG for schizophrenia detection with lower variance. Occipital drives signal under music, frontal under arithmetic. GCN-Transformer on PLV graphs outperforms all baselines.

Abstract:

Multi-condition EEG is a structured physiological
signal whose utility as a clinical digital biomarker
is fundamentally brain-state-dependent—a property ignored by single-condition paradigms that
assume state-invariant biomarkers. We propose
a dual-stream GCN-Transformer on epoch-wise
dynamic ROI-level PLV graphs across six cognitive paradigms on 60 subjects (40 schizophrenia, 20 healthy controls), using structured graph
interventions to quantify state-dependent regional importance. Music perception yields
the strongest digital biomarker signal (84.73%,
±1.23%)—outperforming resting-state by 4.36%
with 2× lower variance—while we observe that
occipital regions are dominant in five of six conditions and frontal regions emerge exclusively under
post-task and working-memory paradigms, with
the same region being informative in one state and
suppressive in another. LOSO validation achieves
92.00±2.45% (Warsaw) and 89.00±2.62% (SUSZ), outperforming all baselines, with a two condition music-arithmetic protocol as a promising biomarker acquisition strategy

Submission Number: 29