Two-Phase Multitask Autoencoder-Based Deep Learning Framework for Subject-Independent EEG Motor Imagery Classification

Electroencephalography (EEG)-based motor imagery (MI) has potential applications in diverse fields including rehabilitation, drone control, and virtual reality. However, its practical use is hindered by low generalization performance in decoding brain signals, primarily due to the subject-dependency...

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Vydané v:	IEEE access Ročník 12; s. 77356 - 77367
Hlavní autori:	Jin, Changgyun, Song, Andrew H., Kim, Seong-Eun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Brain modeling Classification Classification algorithms Data models Decoding Deep learning EEG Electroencephalography Feature extraction Image capture Image classification Machine learning Motor coordination motor imagery multitask autoencoder Reconstruction subject-independent Task analysis Training Virtual reality
ISSN:	2169-3536, 2169-3536
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