Emo-MG Framework: LSTM-based Multi-modal Emotion Detection through Electroencephalography Signals and Micro Gestures

Keyphrases

• Electroencephalography 100%
• Memory-based 100%
• Long Short-term Memory 100%
• Microgestures 100%
• Gesture Framework 100%
• Multimodal Emotion Detection 100%
• Emotion Detection 40%
• Gesture Features 40%
• Result-oriented 10%
• Excellent Performance 10%
• Emotional State 10%
• Emotional Stimuli 10%
• Feature Selection 10%
• Detection Task 10%
• Human-computer Interaction 10%
• Physiological Indicators 10%
• Deep Learning Model 10%
• F1 Score 10%
• Accuracy Score 10%
• Multimodal Interfaces 10%
• Long Short-term Memory Model 10%
• Valence-Arousal-Dominance 10%

Computer Science

• Emotion Detection 100%
• Long Short-Term Memory Network 100%
• Human Computer Interaction 20%
• Deep Learning Model 20%
• Memory Model 20%
• Feature Selection 20%
• Outstanding Performance 20%
• Multimodal Interface 20%
• Emotional State 20%
• Feature Extraction 20%

Psychology

• Electroencephalography 100%
• Short-Term Memory 100%
• Learning Model 14%
• Human Computer Interaction 14%
• Memory Model 14%