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Toward Generating Physiologically Plausible Artificial Electroencephalography Data: Effects of Convolution-based Upsampling Methods on Data Quality

2025-10-14

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<title>Abstract</title> <p>Background: Electroencephalography (EEG) is key for clinical and cognitive research, yet limited EEG data availability restricts deep learning (DL) applications. Generative Adversarial Networks (GAN) based on Convolutional Neural Networks (CNN) can augment EEG datasets. However, the upsampling methods used in CNN-based GANs frequently introduce artifacts into the generated EEG data. Tra...

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Literature Corpus work: efd9287c-c2d7-53cf-ae8d-7d22d912129d
DOI: 10.21203/rs.3.rs-7618908/v1

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