PR-PL: A novel prototypical representation based pairwise learning framework for emotion recognition using EEG signals

Keyphrases

• Learning Framework 100%
• Electroencephalography 100%
• Emotion Recognition 100%
• Pairwise Learning 100%
• Prototypical Representation 100%
• Evaluation Protocol 40%
• Copyright 20%
• Transfer Learning 20%
• Source Domain 20%
• Target Domain 20%
• Benchmark Dataset 20%
• Source Code 20%
• Noise Effects 20%
• Labeling Problem 20%
• Recognition Task 20%
• Noisy Labels 20%
• Labeling Method 20%
• Proximity Relation 20%
• Semantic Structure 20%
• Electroencephalography Data 20%
• Feature Representation 20%
• Affective Computing 20%
• Pseudo-labeling 20%
• Model Stability 20%
• Subjective Feedback 20%
• Prototypical Learning 20%
• Model Reliability 20%
• Feature Separability 20%
• Common Feature Space 20%
• Affective Brain-computer Interface (aBCI) 20%
• Emotional Data 20%
• Label Noise 20%
• Noisy Labeling 20%

Computer Science

• Learning Framework 100%
• Feature Space 50%
• Source Codes 50%
• Transfer Learning 50%
• Computer Interface 50%
• Affective Computing 50%
• Generalizability 50%