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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Veröffentlicht in:	Frontiers in signal processing (Lausanne) Jg. 2
Hauptverfasser:	Salsabilian, Shiva, Najafizadeh, Laleh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Frontiers Media S.A 30.11.2022
Schlagworte:	adversarial regularization cross-subject transfer learning LSTM-long short-term memory mild TBI (mTBI) variational autoencoder (VAE)
ISSN:	2673-8198, 2673-8198
Online-Zugang:	Volltext
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