Entropy-Assisted Multi-Modal Emotion Recognition Framework Based on Physiological Signals
As the result of the growing importance of the Human Computer Interface system, understanding human's emotion states has become a consequential ability for the computer. This paper aims to improve the performance of emotion recognition by conducting the complexity analysis of physiological signals. Based on AMIGOS dataset, we extracted several entropy-domain features such as Refined Composite Multi-Scale Entropy (RCMSE), Refined Composite Multi-Scale Permutation Entropy (RCMPE) from ECG and GSR signals, and Multivariate Multi-Scale Entropy (MMSE), Multivariate Multi-Scale Permutation Entropy (MMPE) from EEG, respectively. The statistical results show that RCMSE in GSR has a dominating performance in arousal, while RCMPE in GSR would be the excellent feature in valence. Furthermore, we selected XGBoost model to predict emotion and get 68% accuracy in arousal and 84% in valence.
Code (0)
등록된 구현이 없습니다.
Tasks
EEGElectroencephalogram (EEG)Emotion RecognitionSimilar Papers 제목 키워드 기반
Speech Emotion Recognition via Entropy-Aware Score Selection
In this paper, we propose a multimodal framework for speech emotion recognition that leverages entropy-aware score selection to combine speech and textual predictions. The proposed method integrates a primary pipeline th…
Speech Emotion RecognitionSentiment AnalysisImproving Speech Emotion Recognition Through Cross Modal Attention Alignment and Balanced Stacking Model
Emotion plays a fundamental role in human interaction, and therefore systems capable of identifying emotions in speech are crucial in the context of human-computer interaction. Speech emotion recognition (SER) is a chall…
Emotion RecognitionSpeech Emotion RecognitionInvestigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition
Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well…
Brain Computer InterfaceClusteringEEGElectroencephalogram (EEG)+3Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features
Use of the electroencephalogram (EEG) and machine learning approaches to recognize emotions can facilitate affective human computer interactions. However, the type of EEG data constitutes an obstacle for cross-individual…
BIG-bench Machine LearningEEGEEG Emotion RecognitionElectroencephalogram (EEG)+1Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition
Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing. However, the unique brain anatomy…
AnatomyEEGEmotion RecognitionUnity