Abstract
Affective brain-computer interface (aBCI) introduces personal affective factors into human-computer interactions, which could potentially enrich the user's experience during the interaction with a computer. However, affective neural patterns are volatile even within the same subject. To maintain satisfactory emotion recognition accuracy, the state-of-the-art aBCIs mainly tailor the classifier to the subject-of-interest and require frequent re-calibrations for the classifier. In this paper, we demonstrate that the recognition accuracy of aBCIs deteriorates when re-calibration is ruled out during the long-term usage for the same subject. Then, we propose a stable feature selection method to choose the most stable affective features, for mitigating the accuracy deterioration to a lesser extent and maximizing the aBCI performance in the long run. We validate our method on a dataset comprising six subjects' EEG data collected during two sessions per day for each subject for eight consecutive days.
Original language | English |
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Title of host publication | Proceedings - 2018 International Conference on Cyberworlds, CW 2018 |
Editors | Alexei Sourin, Olga Sourina, Marius Erdt, Christophe Rosenberger |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 176-183 |
Number of pages | 8 |
ISBN (Electronic) | 9781538673157 |
DOIs | |
Publication status | Published - 26 Dec 2018 |
Event | 17th International Conference on Cyberworlds: CW 2018 - Nanyang Technological University, Singapore, Singapore Duration: 3 Oct 2018 → 5 Oct 2018 https://cw2018.fraunhofer.sg/ http://www.cyberworlds-conference.org/ |
Conference
Conference | 17th International Conference on Cyberworlds |
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Abbreviated title | Cyberworlds |
Country/Territory | Singapore |
City | Singapore |
Period | 3/10/18 → 5/10/18 |
Internet address |
Keywords
- Electroencephalography (EEG)
- Emotion recognition
- Feature selection
- Intra correlation coefficient (ICC)
- Stable feature
ASJC Scopus subject areas
- Signal Processing
- Modelling and Simulation
- Computer Vision and Pattern Recognition
- Artificial Intelligence
Fields of Expertise
- Human- & Biotechnology