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• W4304192912 abstract "Abstract 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." @default.
• W4304192912 created "2022-10-11" @default.
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• W4304192912 date "2022-10-11" @default.
• W4304192912 modified "2023-09-30" @default.
• W4304192912 title "Towards Metacognition: Subject-Aware Contrastive Deep Fusion Representation Learning for EEG Analysis" @default.
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• W4304192912 doi "https://doi.org/10.21203/rs.3.rs-2121897/v1" @default.
• W4304192912 hasPublicationYear "2022" @default.
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