Not All Electrode Channels Are Needed: Knowledge Transfer From Only Stimulated Brain Regions for EEG Emotion Recognition - Perry Fordson, Hayford; Xing, Xiaofen; Guo, Kailing; Xu, Xiangmin (Frontiers in Neuroscience, 2022)
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 has been mostly used due to its ability to detect brain activities in real time and its 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 emotional responses of subjects. In this paper, 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 existing transfer learning method. We apply BRADA to both cross-subject and cross-dataset settings. The experimental results indicate that our proposed transfer learning method can improve valence-arousal emotion recognition tasks.