Research Article
ECG-Based Human Emotion Recognition Across Multiple Subjects
@INPROCEEDINGS{10.1007/978-3-030-23976-3_3, author={Desislava Nikolova and Petia Mihaylova and Agata Manolova and Petia Georgieva}, title={ECG-Based Human Emotion Recognition Across Multiple Subjects}, proceedings={Future Access Enablers for Ubiquitous and Intelligent Infrastructures. 4th EAI International Conference, FABULOUS 2019, Sofia, Bulgaria, March 28-29, 2019, Proceedings}, proceedings_a={FABULOUS}, year={2019}, month={9}, keywords={ECG Affective computing Human emotion recognition Machine learning Artificial Neural Networks Logistic Regression}, doi={10.1007/978-3-030-23976-3_3} }- Desislava Nikolova
Petia Mihaylova
Agata Manolova
Petia Georgieva
Year: 2019
ECG-Based Human Emotion Recognition Across Multiple Subjects
FABULOUS
Springer
DOI: 10.1007/978-3-030-23976-3_3
Abstract
Electrocardiogram (ECG) based affective computing is a new research field that aims to find correlates between human emotions and the registered ECG signals. Typically, emotion recognition systems are personalized, i.e. the discrimination models are subject-dependent. Building subject-independent models is a harder problem due to the high ECG variability between individuals. In this paper, we study the potential of two machine learning methods (Logistic Regression and Artificial Neural Network) to discriminate human emotional states across multiple subjects. The users were exposed to movies with different emotional content (neutral, fear, disgust) and their ECG activity was registered. Based on extracted features from the ECG recordings, the three emotional states were partially discriminated.
