Time and Frequency Domain Features Extraction Comparison for Motor Imagery Detection

Matemáticas

• Feature Extraction 100%
• Imagery 98%
• Frequency Domain 70%
• Time Domain 69%
• Brain-computer Interface 40%
• Random Forest 32%
• Quality of Life 32%
• Strategy 31%
• K-nearest Neighbor 30%
• Elections 29%
• Electroencephalogram (EEG) 29%
• Support Vector Machine 28%
• Classifier 25%
• Machine Learning 24%
• Neural Networks 20%
• Scenarios 20%
• Class 19%
• Model 7%

Ingeniería y ciencia de los materiales

• Feature extraction 51%
• Brain computer interface 25%
• Random forests 23%
• Electroencephalography 21%
• Support vector machines 17%
• Machine learning 16%
• Classifiers 15%
• Neural networks 13%