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Where Does EEG Help Wearable Freezing of Gait Detection? — arXiv:2611.00497

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00497
License (as archived): CC0-1.0
Archived: 2026-11-03T11:19:25.070Z
SHA-256 of the archived text: c484424c13784ee15037f7a1888fb3eab0391cf1164ba6d1d28a061af909831c
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How to cite

Where Does EEG Help Wearable Freezing of Gait Detection? — arXiv:2611.00497 — arXiv (en), https://arxiv.org/abs/2611.00497 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9989ef340efa (Metraipolis archive dc_9989ef340efa, sha256 c484424c13784ee15037f7a1888fb3eab0391cf1164ba6d1d28a061af909831c)

BibTeX

@misc{dc9989ef340efa,
  title = {Where Does EEG Help Wearable Freezing of Gait Detection? — arXiv:2611.00497},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00497},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9989ef340efa; sha256 c484424c13784ee15037f7a1888fb3eab0391cf1164ba6d1d28a061af909831c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00497. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Freezing of gait (FoG) detectors built on inertial measurement units (IMUs) are accurate while the legs move, but they become unreliable when the legs are nearly still, which is exactly when akinetic freezing, voluntary stopping, and standing must be told apart. Electroencephalography (EEG) captures motor-planning activity that leg sensors cannot, but EEG is impractical for daily use. We ask where EEG helps: as an input at inference time, or only during pretraining, so that the deployed detector still needs only IMUs. We assemble a pretraining corpus of synchronized EEG and IMU recordings (5 datasets, 184 people, 94 hours) and compare six settings that differ only in the modalities used for pretraining and as model input, each with tuned hyperparameters and five independent pretraining runs. On the BXHC FoG dataset (leave-one-patient-out, 10 patients with FoG), adding EEG as input raises FoG F1 from 0.663 to 0.740 (9 of 10 patients improve), and the gain in ranking performance is confined to the quartile of windows with the least leg movement (AUROC +0.063). Pretraining the IMU encoder jointly with EEG improves an IMU-only detector both within the cohort (FoG F1 0.663 to 0.705) and when it is trained on a small cohort and tested on another (FoG F1 +0.083, all 10 test patients improve). Repeating the study with PD-only and healthy-only pretraining corpora, each with its own hyperparameter search, shows that IMU-based detection is robust to the corpus, whereas the benefit of aligning EEG with IMU requires both. Cross-cohort analysis further shows that detectors trained without annotated voluntary stops classify nearly all stops as freezing. Finally, on PD versus non-PD classification in three external datasets, joint EEG+IMU pretraining improves PD detection from walking EEG over EEG-only pretraining (AUROC 0.750 vs. 0.683), while IMU-based detection does not depend on the pretraining.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.00497 — 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 2026-11-03T11:02:37.445Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).