A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals
Published 2022 View Full Article
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Title
A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals
Authors
Keywords
BCI, MI, Hybrid neural network, Convolutional neural network, LSTM, Transfer learning
Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 143, Issue -, Pages 105288
Publisher
Elsevier BV
Online
2022-02-10
DOI
10.1016/j.compbiomed.2022.105288
References
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Related references
Note: Only part of the references are listed.- Progress in Brain Computer Interface: Challenges and Opportunities
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- Inter-subject transfer learning with end-to-end deep convolutional neural network for EEG-based BCI
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- Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
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- Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for brain–computer interfaces
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- A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces
- (2009) K.P. Thomas et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
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