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Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis by Michael Briden; Narges Norouzi is a Neuroscience article available to read on EtoBox.

What is Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis about?

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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Author

Michael Briden; Narges Norouzi

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Neuroscience (Life Sciences)