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Aligning the Incomplete: Joint Distribution Calibration for Multimodal EEG-Eye Emotion Recognition — arXiv:2609.39413

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.39413
License (as archived): CC0-1.0
Archived: 2026-10-07T13:44:28.428Z
SHA-256 of the archived text: 996acb96e276b4da9d12358f3a4128f93bfc0d1736352d5c66f8cf2d4b262fd6
Stable URL of this page: https://metraipolis.com/documento/dc_15041beba970

How to cite

Aligning the Incomplete: Joint Distribution Calibration for Multimodal EEG-Eye Emotion Recognition — arXiv:2609.39413 — arXiv (en), https://arxiv.org/abs/2609.39413 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_15041beba970 (Metraipolis archive dc_15041beba970, sha256 996acb96e276b4da9d12358f3a4128f93bfc0d1736352d5c66f8cf2d4b262fd6)

BibTeX

@misc{dc15041beba970,
  title = {Aligning the Incomplete: Joint Distribution Calibration for Multimodal EEG-Eye Emotion Recognition — arXiv:2609.39413},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.39413},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_15041beba970; sha256 996acb96e276b4da9d12358f3a4128f93bfc0d1736352d5c66f8cf2d4b262fd6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.39413. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The success of cross-subject multimodal emotion recognition hinges on maintaining the consistency of the joint data distribution across individuals. However, real-world deployment frequently triggers the \emph{asymmetric joint distribution collapse}: EEG signals suffer from severe cross-subject distribution shifts, while eye movements sensors are susceptible to packet loss and tracking failures. Existing methods treat domain adaptation and missing-modality imputation as disjoint tasks. Consequently, they fail to resolve the compounded errors when both degradations co-occur, either propagating domain shifts through imputed signals or destroying the joint decision boundary. To tackle this unified challenge, we propose GUARD (\textbf{G}radient-guided \textbf{U}nsupervised \textbf{A}symmetric \textbf{R}ecovery of \textbf{D}istributions). First, GUARD establishes a reliable anchor manifold in the source domain by employing a theoretically grounded gradient-weighted objective, which forces the robust EEG modality to preemptively entangle task-discriminative ocular features. Next, to structurally recover the collapsed joint distribution, we constrain a generative module with downstream perceptual losses, prioritizing emotion-discriminative semantics over mere signal fidelity. Finally, we formulate target-domain adaptation as an ill-posed inverse problem. By driving a cycle-consistent flow, we achieve unsupervised calibration of the recovered joint distribution directly on the target-domain manifold. Extensive experiments demonstrate that GUARD significantly outperforms state-of-the-art methods, maintaining resilient discriminative performance even under complete auxiliary modality failure. Our code and models are made publicly available to ensure complete reproducibility.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-01 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2609.39413 — 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 2026-10-07T13:17:28.247Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).