Classification of visual and non-visual learners using electroencephalographic alpha and gamma activities

Computer Science

• Background Knowledge 16%
• classification result 16%
• Component Analysis 16%
• Distance Metric 16%
• Fold Cross Validation 33%
• Healthy Subject 16%
• K-Nearest Neighbor Classifier 50%
• Learning Resource 50%
• Learning Style 16%
• Mahalanobis Distance 16%
• Power Spectral Density 100%
• Principal Components 16%
• Spectral Entropy 16%
• Support Vector Machine 50%
• Wavelet Transforms 100%

Keyphrases

• Optimum Feature 22%