A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features

A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features

ObjectiveExtracting deep features from participants’ bioelectric signals and constructing models are key research directions in motor imagery (MI) classification tasks. In this study, we constructed a multimodal multitask hybrid brain-computer interface net (2M-hBCINet) based on deep features of ele...

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Bibliographic Details
Main Authors: Yingyu Cao, Shaowei Gao, Huixian Yu, Zhenxi Zhao, Dawei Zang, Chun Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-12-01
Series:Frontiers in Physiology
Subjects:
motor imagery
hybrid brain-computer interface
variational autoencoder
channel attention mechanism
multitask learning
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2024.1487809/full

Internet

https://www.frontiersin.org/articles/10.3389/fphys.2024.1487809/full

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