Motor Imagery Classification Using Inter-Task Transfer Learning via a Channel-Wise Variational Autoencoder-Based Convolutional Neural Network

Keyphrases

• Motor Imagery Classification 100%
• Electroencephalography 100%
• Brain-computer Interface 100%
• Convolutional Neural Network 100%
• Motor Execution 100%
• Variational Autoencoder 100%
• Task Transfer 100%
• Channel-wise 100%
• Motor Imagery 66%
• Small Sample Size 33%
• Upper Limb 33%
• Classification Performance 33%
• Classification Accuracy 33%
• Calibration Data 33%
• Kinematic Data 33%
• Brain Signals 33%
• Number of Data 33%
• Transfer Learning 33%
• Forearm 33%
• Stable Performance 33%
• Learning Strategies 33%
• Performance Increase 33%
• MI-EEG 33%
• Imagined Movement 33%
• Movement Decoding 33%
• Deep Learning 33%
• Horizon 2020 33%
• Forearm Movements 33%
• Movement Task 33%
• Interface Learning 33%
• Model Training 33%
• Public Dataset 33%
• Everyday Life 33%
• Healthy Participants 33%
• Time Calibration 33%
• Same Limb 33%
• Interface Study 33%

Computer Science

• Computer Interface 100%
• Transfer Learning 100%
• Convolutional Neural Network 100%
• Variational Autoencoder 100%
• Classification Performance 33%
• Classification Accuracy 33%
• Training Model 33%
• Conventional Model 33%
• Deep Learning Method 33%