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Robust EEG-based Emotion Recognition from Coarse Trial-level Labels under Intra-trial Affective Variability — arXiv:2611.01731

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01731
License (as archived): CC0-1.0
Archived: 2026-11-03T18:18:09.755Z
SHA-256 of the archived text: 03c6dbe2941c2c76344239a07fb5c3e4f5d162fb9481dc8de58ea0231e5c554f
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How to cite

Robust EEG-based Emotion Recognition from Coarse Trial-level Labels under Intra-trial Affective Variability — arXiv:2611.01731 — arXiv (en), https://arxiv.org/abs/2611.01731 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4e37fa9ea2f0 (Metraipolis archive dc_4e37fa9ea2f0, sha256 03c6dbe2941c2c76344239a07fb5c3e4f5d162fb9481dc8de58ea0231e5c554f)

BibTeX

@misc{dc4e37fa9ea2f0,
  title = {Robust EEG-based Emotion Recognition from Coarse Trial-level Labels under Intra-trial Affective Variability — arXiv:2611.01731},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01731},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4e37fa9ea2f0; sha256 03c6dbe2941c2c76344239a07fb5c3e4f5d162fb9481dc8de58ea0231e5c554f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01731. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

EEG-based emotion recognition has become an active research area in brain-computer interfaces. However, fine-grained affective annotation is costly, so most datasets provide only a global label for each long EEG trial. For practical emotion recognition, trials are commonly divided into short temporal windows, with the global label propagated to all windows for training. This practice treats trial-level emotion as temporally uniform, although affective responses may fluctuate within a trial. Consequently, windows that weakly reflect or deviate from the global emotional state may receive unreliable labels, posing a key challenge to robust EEG-based emotion recognition. To address this challenge, we propose VARY, a variability-aware robust learning framework for EEG-based emotion recognition from coarse trial-level labels. VARY incorporates a Two-stage Sample Selection (TSS) strategy to identify EEG windows with reliable emotional labels, and introduces MixAug, a data augmentation method tailored for EEG to improve model robustness under limited and unreliable supervision. We comprehensively evaluate VARY under the setting of label-noise learning on two public datasets, using three network architectures and multiple label-noise settings. Our method demonstrates superior performance, achieving a maximum accuracy gain of 11.54% on the DREAMER dataset and 5.39% on the SEED dataset compared with the best-performing baseline, highlighting the effectiveness of VARY in learning from unreliable trial-level labels under intra-trial affective variability.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01731 — 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 2026-11-03T18:09:53.624Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).