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Leakage-Safe Empirical Benchmarking of EEG-Based Machine Learning Pipelines for Dementia Classification — arXiv:2609.22092

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.22092
License (as archived): CC0-1.0
Archived: 2026-10-09T07:08:00.490Z
SHA-256 of the archived text: 7851d3325718fe0679420dea5bbeff45f72df6cb5bb5b510dfa53b37ae5559f2
Stable URL of this page: https://metraipolis.com/documento/dc_3fbbde656c2e

How to cite

Leakage-Safe Empirical Benchmarking of EEG-Based Machine Learning Pipelines for Dementia Classification — arXiv:2609.22092 — arXiv (en), https://arxiv.org/abs/2609.22092 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3fbbde656c2e (Metraipolis archive dc_3fbbde656c2e, sha256 7851d3325718fe0679420dea5bbeff45f72df6cb5bb5b510dfa53b37ae5559f2)

BibTeX

@misc{dc3fbbde656c2e,
  title = {Leakage-Safe Empirical Benchmarking of EEG-Based Machine Learning Pipelines for Dementia Classification — arXiv:2609.22092},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.22092},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3fbbde656c2e; sha256 7851d3325718fe0679420dea5bbeff45f72df6cb5bb5b510dfa53b37ae5559f2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.22092. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Electroencephalography (EEG) is a low-cost and non-invasive signal source for dementia screening, yet existing EEG-based studies remain difficult to compare because preprocessing, EEG segmentation, feature design, classifier choice, and validation protocols vary across studies and are often evaluated in isolation. This variability limits the derivation of robust pipeline recommendations. This paper presents a leakage-safe empirical benchmark for resting-state EEG-based dementia classification and uses it to identify a practical best-practice pipeline. Using the public OpenNeuro ds004504 dataset, the benchmark evaluates artifact correction, fixed-length EEG segmentation, training-only augmentation, multi-domain feature extraction, fold-internal feature selection, classical machine-learning (ML) classifiers, subject-level aggregation, and interpretation under leave-one-subject-out (LOSO) validation. The best-performing pipeline in this benchmark combines Artifact Subspace Reconstruction (ASR) followed by Independent Component Analysis (ICA), 10 s EEG epochs with training-only amplitude scaling and Gaussian-noise augmentation, spectral, complexity, and pairwise connectivity features, mutual-information top-100 selection, linear support vector machine classification, and mean-probability aggregation. It achieves 87.69 percent accuracy, 88.89 percent F1-score, and 91.20 percent AUC on Alzheimer disease (AD) versus cognitively normal controls (CN) classification while retaining 100 features from 1596 raw descriptors. The results support compact multi-domain EEG features as an interpretable and leakage-safe baseline for subject-level dementia classification.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-22 in cs.LG).
Categorie arXiv: Signal Processing (eess.SP) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.22092 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T06:58:09.467Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).