A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
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
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Progress in Brain Computer Interface: Challenges and Opportunities
- (2021) Simanto Saha et al. Frontiers in Systems Neuroscience
- Hybrid deep neural network using transfer learning for EEG motor imagery decoding
- (2020) Ruilong Zhang et al. Biomedical Signal Processing and Control
- Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals
- (2019) Zied Tayeb et al. SENSORS
- A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
- (2019) Yong Yu et al. NEURAL COMPUTATION
- A novel hybrid deep learning scheme for four-class motor imagery classification
- (2019) Zhang Ruilong et al. Journal of Neural Engineering
- Soft Computing-Based EEG Classification by Optimal Feature Selection and Neural Networks
- (2019) Muhammad Hamza Bhatti et al. IEEE Transactions on Industrial Informatics
- Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
- (2019) Syed Umar Amin et al. Future Generation Computer Systems-The International Journal of eScience
- Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy
- (2019) Kevin de Haan et al. PROCEEDINGS OF THE IEEE
- EEGNet: a compact convolutional neural network for EEG-based brain--computer interfaces
- (2018) Vernon Lawhern et al. Journal of Neural Engineering
- Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification
- (2018) Antonio Maria Chiarelli et al. Journal of Neural Engineering
- Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)
- (2018) Sebastian Michelmann et al. JOURNAL OF NEUROSCIENCE METHODS
- Drug-drug interaction extraction from biomedical texts using long short-term memory network
- (2018) Sunil Kumar Sahu et al. JOURNAL OF BIOMEDICAL INFORMATICS
- Inter-subject transfer learning with end-to-end deep convolutional neural network for EEG-based BCI
- (2018) Fatemeh Fahimi et al. Journal of Neural Engineering
- Classification of multiple motor imagery using deep convolutional neural networks and spatial filters
- (2018) Brenda E. Olivas-Padilla et al. APPLIED SOFT COMPUTING
- Deep learning with convolutional neural networks for EEG decoding and visualization
- (2017) Robin Tibor Schirrmeister et al. HUMAN BRAIN MAPPING
- Error detection and accuracy estimation in automatic speech recognition using deep bidirectional recurrent neural networks
- (2017) Atsunori Ogawa et al. SPEECH COMMUNICATION
- Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
- (2016) Hoo-Chang Shin et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for brain–computer interfaces
- (2011) Yongwoong Jeon et al. INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS
- Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms
- (2010) Fabien Lotte et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces
- (2009) K.P. Thomas et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreDiscover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversation