Subject-invariant feature learning for mTBI identification using LSTM-based variational autoencoder with adversarial regularization

Developing models for identifying mild traumatic brain injury (mTBI) has often been challenging due to large variations in data from subjects, resulting in difficulties for the mTBI-identification models to generalize to data from unseen subjects. To tackle this problem, we present a long short-term...

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Vydané v:	Frontiers in signal processing (Lausanne) Ročník 2
Hlavní autori:	Salsabilian, Shiva, Najafizadeh, Laleh
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Frontiers Media S.A 30.11.2022
Predmet:	adversarial regularization cross-subject transfer learning LSTM-long short-term memory mild TBI (mTBI) variational autoencoder (VAE)
ISSN:	2673-8198, 2673-8198
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