Stable feature selection for EEG-based emotion recognition

Zirui Lan, Olga Sourina, Lipo Wang, Yisi Liu, Reinhold Scherer, Gernot R. Müller-Putz

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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 languageEnglish
Title of host publicationProceedings - 2018 International Conference on Cyberworlds, CW 2018
EditorsAlexei Sourin, Olga Sourina, Marius Erdt, Christophe Rosenberger
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781538673157
Publication statusPublished - 26 Dec 2018
Event17th International Conference on Cyberworlds: CW 2018 - Nanyang Technological University, Singapore, Singapore
Duration: 3 Oct 20185 Oct 2018


Conference17th International Conference on Cyberworlds
Abbreviated titleCyberworlds
Internet address


  • 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


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