An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain-computer interface

Keyphrases

• Brain-computer Interface 100%
• Classification Algorithms 100%
• Rhythmic Components 100%
• MI-EEG 100%
• Motor Imagery Task 100%
• Synthesis Strategies 100%
• Left-handed 50%
• Human Subjects 50%
• Classification Accuracy 50%
• Algorithm Analysis 50%
• Success Rate 50%
• Principal Coordinate Analysis (PCoA) 50%
• Hand Movement 50%
• Feature Space 50%
• Training Process 50%
• Recognition-based 50%
• Instantaneous Power 50%
• EEG Signals 50%
• Mental States 50%
• Frequency Weighting 50%
• Generalization Ability 50%
• Interface Research 50%
• Downsampling 50%
• Frequency Decomposition 50%
• EEG-based Brain-computer Interface 50%
• Mental State Recognition 50%
• Feature Signal 50%
• Linear Discriminate Analysis 50%

Computer Science

• Computer Interface 100%
• Classification Algorithm 100%
• Feature Space 50%
• Training Process 50%
• Classification Accuracy 50%
• Principal Components 50%
• Component Analysis 50%
• Instantaneous Power 50%
• Frequency Decomposition 50%

Neuroscience

• Brain-Computer Interface 100%
• Electroencephalography 100%