Common Spatial Pattern and Riemannian Manifold-Based Real-Time Multiclass Motor Imagery EEG Classification

Keyphrases

• Classification Performance 100%
• MI-EEG 100%
• Common Spatial Pattern 100%
• EEG Classification 100%
• Multi-class Motor Imagery 100%
• Riemannian Manifold 100%
• Computation Time 66%
• Machine Learning Algorithms 66%
• BCI Competition 66%
• BCI Competition IV 66%
• Hyperplane 33%
• Classification Algorithms 33%
• Classification Methods 33%
• Computational Requirements 33%
• Deep Learning Algorithm 33%
• Portable Device 33%
• Memory Consumption 33%
• Memory Time 33%
• Feature Quality 33%
• Peak Memory 33%
• Present Challenges 33%
• Subject-specific 33%
• Raspberry Pi 33%
• Motor Imagery Classification 33%
• Portable Applications 33%
• Robustness Issues 33%
• Tangent Space Mapping 33%
• Jetson Nano 33%
• Feature Robustness 33%
• Riemannian Tangent Space 33%

Computer Science

• Computation Time 100%
• Classification Performance 100%
• Spatial Pattern 100%
• Machine Learning 100%
• Learning System 100%
• Experimental Result 50%
• Support Vector Machine 50%
• Classification Method 50%
• Portable Device 50%
• Raspberry Pi 50%
• Portable Application 50%
• Deep Learning Method 50%
• Hyperplane 50%

Engineering

• Brain-Computer Interface 100%
• Motor Imagery 100%
• Common Spatial Pattern 100%
• Computation Time 50%
• Classification Performance 50%
• Learning System 50%
• Experimental Result 25%
• Classification Method 25%
• Data Point 25%
• Portable Device 25%
• Portable Application 25%
• Tangent Space 25%
• Deep Learning Method 25%
• Hyperplane 25%