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METER: Matched-Event Calibration for Short-Event EEG Microstate Transition Analysis — arXiv:2611.02045

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02045
License (as archived): CC0-1.0
Archived: 2026-11-03T18:37:08.481Z
SHA-256 of the archived text: 10d2caf2965fcefdd3dc9acc3efc51fbb92350f49d744857d2b0bb8dd650756f
Stable URL of this page: https://metraipolis.com/documento/dc_bcbf0941bb00

How to cite

METER: Matched-Event Calibration for Short-Event EEG Microstate Transition Analysis — arXiv:2611.02045 — arXiv (en), https://arxiv.org/abs/2611.02045 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bcbf0941bb00 (Metraipolis archive dc_bcbf0941bb00, sha256 10d2caf2965fcefdd3dc9acc3efc51fbb92350f49d744857d2b0bb8dd650756f)

BibTeX

@misc{dcbcbf0941bb00,
  title = {METER: Matched-Event Calibration for Short-Event EEG Microstate Transition Analysis — arXiv:2611.02045},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02045},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bcbf0941bb00; sha256 10d2caf2965fcefdd3dc9acc3efc51fbb92350f49d744857d2b0bb8dd650756f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02045. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Short cognitive events contain unequal and often sparse transition evidence, so raw EEG microstate distances can reflect finite sampling rather than neural reconfiguration. We introduce METER (Matched-Event Transition Estimation against a Reference), a participant-relative framework that separates marginal occupancy from conditional next-state choice and evaluates each event against duration-conditioned events from a fixed participant-specific reference bank. METER-Jump integrates uncertainty in sparse target and reference transition tables, returning a randomized event rank for testing and a continuous standardized coordinate for association analysis. Using a reproducible three-state representation selected by spatial criteria, we evaluated METER with participant-fitted simulations, estimator ablations, map perturbations, and preprocessing sensitivities. Event-level false-positive rates were 0.044-0.056 across four heterogeneous null mechanisms. By contrast, global cutoffs learned under the participant-fitted null and transferred unchanged to other regimes produced false-positive rates from 0.004 under rare transitions to 0.234 under map perturbation; duration-conditioned ranks remained near 0.05. Detection probability was approximately 0.69 with fixed maps but fell to 0.22 after bootstrap-derived map-assignment perturbation. In 4,175 natural-reading events from 18 participants, no linguistic association survived multiplicity correction in the primary 30-ms analysis, although a 20-ms sensitivity changed one within-stream max-$T$ decision. METER therefore calibrates finite-event departures conditional on a defined microstate sequence, while map assignment, filtering, temporal smoothing, and reference construction remain separate sources of measurement uncertainty.

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/2611.02045 — 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 2026-11-03T18:12:54.539Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).