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The framework, called\n                    <jats:italic>WaveFusion<\/jats:italic>\n                    , is composed of lightweight convolutional neural networks for per-lead time\u2013frequency 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.\n                  <\/jats:p>","DOI":"10.1007\/s00422-023-00967-8","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T09:05:00Z","timestamp":1688979900000},"page":"363-372","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis"],"prefix":"10.1007","volume":"117","author":[{"given":"Michael","family":"Briden","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Narges","family":"Norouzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"967_CR1","doi-asserted-by":"publisher","first-page":"86899","DOI":"10.1109\/ACCESS.2021.3089358","volume":"9","author":"A Al-Ezzi","year":"2021","unstructured":"Al-Ezzi A, Yahya N, Kamel N et al (2021) Severity assessment of social anxiety disorder using deep learning models on brain effective connectivity. 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