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.
Originalsprache | englisch |
---|---|
Titel | Proceedings - 2018 International Conference on Cyberworlds, CW 2018 |
Redakteure/-innen | Alexei Sourin, Olga Sourina, Marius Erdt, Christophe Rosenberger |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers |
Seiten | 176-183 |
Seitenumfang | 8 |
ISBN (elektronisch) | 9781538673157 |
DOIs | |
Publikationsstatus | Veröffentlicht - 26 Dez. 2018 |
Veranstaltung | 17th International Conference on Cyberworlds: CW 2018 - Nanyang Technological University, Singapore, Singapur Dauer: 3 Okt. 2018 → 5 Okt. 2018 https://cw2018.fraunhofer.sg/ http://www.cyberworlds-conference.org/ |
Konferenz
Konferenz | 17th International Conference on Cyberworlds |
---|---|
Kurztitel | Cyberworlds |
Land/Gebiet | Singapur |
Ort | Singapore |
Zeitraum | 3/10/18 → 5/10/18 |
Internetadresse |
ASJC Scopus subject areas
- Signalverarbeitung
- Modellierung und Simulation
- Maschinelles Sehen und Mustererkennung
- Artificial intelligence
Fields of Expertise
- Human- & Biotechnology