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Towards Subject Agnostic Affective Emotion Recognition
University Of Surrey, Guildford, United Kingdom.
University Of Southampton, Southampton, United Kingdom.
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-2851-4260
2023 (English)In: CEUR Workshop Proceedings: Proceedings of the 2nd International Workshop on Multimodal Human Understanding for the Web and Social Media / [ed] Gullal S. Cheema; Sherzod Hakimov; Marc A. Kastner; Noa Garcia, Aachen: Rheinisch-Westfaelische Technische Hochschule Aachen , 2023, Vol. 3566, p. 47-61Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on affective emotion recognition, aiming to perform in the subject-agnostic paradigm based on EEG signals. However, EEG signals manifest subject instability in subject-agnostic affective Brain-computer interfaces (aBCIs), which led to the problem of distributional shift. Furthermore, this problem is alleviated by approaches such as domain generalisation and domain adaptation. Typically, methods based on domain adaptation confer comparatively better results than the domain generalisation methods but demand more computational resources given new subjects. We propose a novel framework, meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our domain adaptation approach is augmented through meta-learning, which consists of a recurrent neural network, a classifier, and a distributional shift controller based on a sum-decomposable function. Also, we present that a neural network explicating a sum-decomposable function can effectively estimate the divergence between varied domains. The network setting for augmented domain adaptation follows meta-learning and adversarial learning, where the controller promptly adapts to new domains employing the target data via a few self-adaptation steps in the test phase. Our proposed approach is shown to be effective in experiments on a public aBICs dataset and achieves similar performance to state-of-the-art domain adaptation methods while avoiding the use of additional computational resources. © 2023 Copyright for this paper by its authors.

Place, publisher, year, edition, pages
Aachen: Rheinisch-Westfaelische Technische Hochschule Aachen , 2023. Vol. 3566, p. 47-61
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 3566
Keywords [en]
Domain adaptation, EEG, Emotion recognition
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-52362Scopus ID: 2-s2.0-85180126187OAI: oai:DiVA.org:hh-52362DiVA, id: diva2:1825968
Conference
2nd International Workshop on Multimodal Human Understanding for the Web and Social Media, MUWS 2023, Birmingham, United Kingdom, 22 October, 2023
Available from: 2024-01-10 Created: 2024-01-10 Last updated: 2024-01-10Bibliographically approved

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Tiwari, Prayag

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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