CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis

Keyphrases

• Spectrogram Image 100%
• XGBoost 100%
• Affective State Recognition 100%
• Convolutional Neural Network 100%
• Image Analysis 100%
• Feature Fusion 75%
• Valence 50%
• Fusion Method 50%
• Extreme Gradient Boosting (XGBoost) Classifier 50%
• Image-based 50%
• EEG Signals 50%
• Arousal 50%
• Support Vector Machine 25%
• Support Vector Machine Model 25%
• Chi-square 25%
• Neural Network 25%
• Negative Emotions 25%
• Statistical Features 25%
• Two-dimensional Convolutional Neural Network 25%
• Calm 25%
• Recursive Feature Elimination 25%
• Multiple Stimuli 25%
• RGB Image 25%
• Performance Studies 25%
• Power Spectral Density 25%
• Negative Dominance 25%
• Emotional State 25%
• Benchmark Dataset 25%
• Feature Vector 25%
• Hjorth Parameters 25%
• Discrete Cosine Transform 25%
• Fused Features 25%
• Fast Fourier Transformation 25%
• Self-evaluation 25%
• Spectrogram 25%
• Active Research 25%
• Emotion Levels 25%
• Human Emotions 25%
• XGBoost Method 25%
• Short-time Fourier Transform 25%
• Discriminative Features 25%
• Emotion Recognition 25%
• Dreamers 25%
• Convolutional Neural Network Features 25%
• Feature Extraction Methods 25%
• Dense Layer 25%
• Emotion Dimensions 25%
• Brain Signals 25%
• Positive Emotions 25%
• Human Emotion Detection 25%
• Arousal-valence 25%

Computer Science

• Image Analysis 100%
• Convolutional Neural Network 100%
• XGBoost 100%
• Support Vector Machine 25%
• Feature Fusion 25%
• Neural Network 12%
• Discriminative Feature 12%
• RGB Image 12%
• Emotion Detection 12%
• Fast Fourier Transformations 12%
• Feature Vector 12%
• Active Research 12%
• Time Fourier Transform 12%
• Emotional State 12%
• Statistical Feature 12%
• Feature Extraction 12%
• Power Spectral Density 12%

Neuroscience

• Emotion 100%
• Neural Network 100%
• Support Vector Machine 28%
• Cell Excitability 14%
• Fourier Transform 14%