Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

Keyphrases

• Transformative Learning 100%
• Feature Extraction 100%
• Machine Learning Techniques 100%
• Feature Classification 100%
• EEG Signals 100%
• Wavelet Transform 100%
• Discrete Wavelet Transform 33%
• Detailed Coefficients 33%
• Relative Wavelet Energy 33%
• Accuracy Rate 16%
• Performance Measures 16%
• Feature Extraction Methods 16%
• Support Vector Machine 16%
• Sensitivity Specificity 16%
• Multilayer Perceptron 16%
• Accuracy Value 16%
• Decomposition Level 16%
• Proposed Feature Extraction 16%
• Complex Cognitive Task 16%
• Precision Parameters 16%
• Extraction Scheme 16%
• EEG Dataset 16%
• Approximation Coefficients 16%
• Wavelet Energy 16%
• K-nearest 16%

Engineering

• Machine Learning Technique 100%
• Feature Extraction 100%
• Recorded Signal 100%
• Energy Engineering 66%
• Tasks 33%
• Metrics 33%
• Nearest Neighbor 33%
• Perceptron 33%
• Performance Measure ψ 33%
• Support Vector Machine 33%
• Based Feature Extraction 33%
• Decomposition Level 33%
• Neighbor Classifier 33%

Computer Science

• Feature Extraction 100%
• Machine Learning Technique 100%
• Wavelet Transforms 100%
• Approximation (Algorithm) 66%
• Performance Measure 33%
• Support Vector Machine 33%
• Multilayer Perceptron 33%
• Nearest Neighbors Classifier 33%
• Decomposition Level 33%