Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks
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Title
Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks
Authors
Keywords
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Journal
Computational and Mathematical Methods in Medicine
Volume 2020, Issue -, Pages 1-10
Publisher
Hindawi Limited
Online
2020-08-29
DOI
10.1155/2020/1683013
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Related references
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- (2020) Yuying Rong et al. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
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- (2019) Nitesh Singh Malan et al. COMPUTERS IN BIOLOGY AND MEDICINE
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- (2018) Zijian Wang et al. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
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- A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
- (2018) F Lotte et al. Journal of Neural Engineering
- System Framework of Robotics in Upper Limb Rehabilitation on Poststroke Motor Recovery
- (2018) Kai Zhang et al. BEHAVIOURAL NEUROLOGY
- A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines
- (2017) Na Lu et al. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
- Provision of somatosensory inputs during motor imagery enhances learning-induced plasticity in human motor cortex
- (2017) Gaia Bonassi et al. Scientific Reports
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- Application of Covariate Shift Adaptation Techniques in Brain–Computer Interfaces
- (2010) Yan Li et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- A Survey on Transfer Learning
- (2009) Sinno Jialin Pan et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface
- (2008) M. PHOTHISONOTHAI et al. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
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