Publications
Detailed Information
EEG-based Emotion Detection Using Unsupervised Transfer Learning
Cited 29 time in
Web of Science
Cited 31 time in Scopus
- Authors
- Issue Date
- 2019-07
- Publisher
- IEEE Service Center
- Citation
- Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, pp.694-697
- Abstract
- Emotion classification using EEG signal processing has the potential of significantly improving the social integration of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS) or the acute stages of Alzheimer's disease. One important challenge to the implementation of high-fidelity emotion recognition systems is the inadequacy of EEG data in terms of Signal-to-noise ratio (SNR), duration, and subject-to-subject variability. In this paper, we present a novel, integrated framework for semigeneric emotion detection using (1) independent component analysis for EEG preprocessing, (2) EEG subject clustering by unsupervised learning, and (3) a convolutional neural network (CNN) for EEG-based emotion recognition. The training and testing data was built using the combination of two publicly available repositories (DEAP and DREAMER), and a local dataset collected at Khalifa University using the standard International Affective Picture System (IAPS). The CNN classifier with the proposed transfer learning approach achieves an average accuracy of 70.26% for valence and 72.42% for arousal, which are superior to the reported accuracies of all generic (subject-independent) emotion classifiers.
- ISSN
- 1557-170X
- Files in This Item:
- There are no files associated with this item.
Related Researcher
- College of Engineering
- Department of Electrical and Computer Engineering
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.