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Leakage-proof multi-view EEG pipeline for ADHD classification with aperiodic-aware Riemannian robust late-fusion evaluation
ID：165 View Protection：ATTENDEE Updated Time：2025-12-23 13:29:19 Hits：400 Online

Start Time：2025-12-29 16:15（Asia/Amman）

Duration：15min

Session：S4 Track 4: Dedicated Technologies for Wireless Networks Track 6: Signal Processing for Wireless Communications Track 8: Communication and Networking Technologies for Smart Agriculture » S4Track 4: Dedicated Technologies for Wireless NetworksTrack 6: Signal Processing for Wireless CommunicationsTrack 8: Communication and Networking Technologies for Smart Agriculture

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Abstract
We present a leakage-proof, multi-view EEG framework for ADHD classification that fuses four complementary signals: 1) aperiodic-aware spectra that separate oscillatory peaks from the 1/f background and yield a corrected θ/β* index; 2) spatial structure via Riemannian geometry on covariance (SPD→Tangent); 3) sub-second microstate dynamics (coverage, dwell, transitions, entropy); and 4) lightweight self-supervised embeddings from a compact TCN/Transformer trained strictly within the training fold. A regularized late-fusion stage aggregates calibrated probabilities (isotonic/Platt), and the full pipeline is trained/frozen under nested Group/LOSO cross-validation with a locked external holdout to prevent subject-level leakage. On pediatric EEG (N=121), the method attains balanced accuracy ≈93.5% (±3.0) with ROC–AUC ≈0.97 and PR–AUC ≈0.96; on a cross-dataset holdout, performance remains high (BA ≈91%, Δ≈−2–3 pp), indicating true out-of-subject generalization. Robustness checks show minimal sensitivity to referencing (CAR vs. linked mastoids, Δ≤0.3 pp) and modest gains with longer recordings (≥4 min → +~0.7 pp BA); Riemannian shrinkage λ≈10⁻³ is near-optimal. Label-permutation and subject-shuffle collapse to chance (BA≈50%, AUC≈0.50), supporting validity. Overall, the framework’s oscillation-aware, geometry-respecting, dynamics-sensitive, and SSL-enhanced design delivers accurate, calibrated predictions suitable for high-specificity clinical triage and prospective deployment. By advancing reliable, data-driven neurodiagnostic tools, our approach can improve early ADHD screening and equitable access to high-quality mental health assessment.
Keywords
EEG, ADHD, Leakage-proof, Riemannian geometry, Self-supervised learning.
Speaker
Mohamadreza Khosravi
Researcher Shiraz University of Medical Sciences

Submission Author
Khosro Rezaee Meybod University
Mohamadreza Khosravi Shiraz University of Medical Sciences
Ali Rachini Holy Spirit University of Kaslik
Zakaria Che Muda Surveying INTI-IU University
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Important Date

  • Conference Date

    Dec 29

    2025

    to

    Dec 31

    2025

  • Dec 20 2025

    Draft paper submission deadline

  • Dec 31 2025

    Contribution Submission Deadline

  • Dec 31 2025

    Registration deadline

Sponsored By
United Societies of Science
Organized By
Zarqa University
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