A self-supervised reconstruction framework for segment-level detection of task-induced deviations in multi-channel EEG

Authors

Antonio Quintero-Rincón (ORCID: https://orcid.org/0000-0003-0186-4049)
Lucas A. Saavedra (ORCID: https://orcid.org/0000-0002-3566-1154)
Hadj Batatia (ORCID: https://orcid.org/0000-0003-0433-2152)

Institutions

Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
Pontificia Universidad Católica Argentina (AR)
Biomed Research Institute (US)
Heriot-Watt University Dubai (AE)

Publication Details

Journal: PLoS ONE
Published: 2026-10-30
DOI: https://doi.org/10.1371/journal.pone.0361586
Primary Topic: EEG and Brain-Computer Interfaces
Type: article
Field-Weighted Citation Impact: 0.00

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article

A self-supervised reconstruction framework for segment-level detection of task-induced deviations in multi-channel EEG

Antonio Quintero-Rincón, Lucas A. Saavedra, Hadj Batatia

Oct 30, 2026

1 links

PLoS ONE

EEG and Brain-Computer Interfaces

article

A self-supervised reconstruction framework for segment-level detection of task-induced deviations in multi-channel EEG

Antonio Quintero-Rincón, Lucas A. Saavedra, Hadj Batatia

October 30, 2026article en

Abstract

No abstract available for this paper.

PLoS ONEVol. 21(10)

Consejo Nacional de Investigaciones Científicas y Técnicas (AR), Pontificia Universidad Católica Argentina (AR), Biomed Research Institute (US), Heriot-Watt University Dubai (AE)

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Openalex Percentile: Top 14%

EEG and Brain-Computer Interfaces

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