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Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis

We propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects’ confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time–frequency ana...

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Detalles Bibliográficos
Autores principales: Briden, Michael, Norouzi, Narges
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600301/
https://www.ncbi.nlm.nih.gov/pubmed/37402000
http://dx.doi.org/10.1007/s00422-023-00967-8
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author Briden, Michael
Norouzi, Narges
author_facet Briden, Michael
Norouzi, Narges
author_sort Briden, Michael
collection PubMed
description We propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects’ confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time–frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity within a multi-subject electroencephalogram dataset to boost representation learning and classification accuracy. The WaveFusion framework demonstrates high accuracy in classifying confidence levels by achieving a classification accuracy of 95.7% while also identifying influential brain regions.
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spelling pubmed-106003012023-10-27 Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis Briden, Michael Norouzi, Narges Biol Cybern Original Article We propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects’ confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time–frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity within a multi-subject electroencephalogram dataset to boost representation learning and classification accuracy. The WaveFusion framework demonstrates high accuracy in classifying confidence levels by achieving a classification accuracy of 95.7% while also identifying influential brain regions. Springer Berlin Heidelberg 2023-07-04 2023 /pmc/articles/PMC10600301/ /pubmed/37402000 http://dx.doi.org/10.1007/s00422-023-00967-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Briden, Michael
Norouzi, Narges
Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
title Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
title_full Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
title_fullStr Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
title_full_unstemmed Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
title_short Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis
title_sort toward metacognition: subject-aware contrastive deep fusion representation learning for eeg analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600301/
https://www.ncbi.nlm.nih.gov/pubmed/37402000
http://dx.doi.org/10.1007/s00422-023-00967-8
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