Not All Electrode Channels Are Needed: Knowledge Transfer From Only Stimulated Brain Regions for EEG Emotion Recognition

Hayford Perry Fordson; Xiaofen Xing; Kailing Guo; Xiangmin Xu

doi:10.3389/fnins.2022.865201

Not All Electrode Channels Are Needed: Knowledge Transfer From Only Stimulated Brain Regions for EEG Emotion Recognition

Front Neurosci. 2022 May 24:16:865201. doi: 10.3389/fnins.2022.865201. eCollection 2022.

Authors

Hayford Perry Fordson¹, Xiaofen Xing¹, Kailing Guo¹, Xiangmin Xu^{1

2}

Affiliations

¹ School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
² School of Future Technology, South China University of Technology, Guangzhou, China.

Abstract

Emotion recognition from affective brain-computer interfaces (aBCI) has garnered a lot of attention in human-computer interactions. Electroencephalographic (EEG) signals collected and stored in one database have been mostly used due to their ability to detect brain activities in real time and their reliability. Nevertheless, large EEG individual differences occur amongst subjects making it impossible for models to share information across. New labeled data is collected and trained separately for new subjects which costs a lot of time. Also, during EEG data collection across databases, different stimulation is introduced to subjects. Audio-visual stimulation (AVS) is commonly used in studying the emotional responses of subjects. In this article, we propose a brain region aware domain adaptation (BRADA) algorithm to treat features from auditory and visual brain regions differently, which effectively tackle subject-to-subject variations and mitigate distribution mismatch across databases. BRADA is a new framework that works with the existing transfer learning method. We apply BRADA to both cross-subject and cross-database settings. The experimental results indicate that our proposed transfer learning method can improve valence-arousal emotion recognition tasks.

Keywords: EEG; brain region; channel selection; domain adaptation; emotion recognition; transfer learning.