Cross-subject And Cross-device Wearable EEG Emotion Recognition Using Frontal EEG Under Virtual Reality Scenes

In recent years, with the rise of brain computer interface, automatic emotion recognition based on Electroencephalography (EEG) has attracted more and more attention. However, the emotional stimuli used in the existing studies are limited to music, pictures and videos, which cannot induce emotion well. Virtual reality (VR) can provide highly immersive 3D scenes, which can accurately and effectively induce emotion. Therefore, our work induced the subjects' emotions through VR scenes, and collected the frontal EEG data based on wearable technology, innovatively proposed a VR-induced wearable frontal EEG emotion recognition dataset, which contains two sub datasets for cross-subject and cross-device research. Based on the dataset, we proposed a multi-spatial domain adaptation network (MSDAN) to eliminate the differences caused by individuals and EEG acquisition devices, and improve the generalization performance of the model in complex situations. MSDAN aimed to align the feature distributions of the source and target domains in multiple spaces and obtain the common features related to emotion. In the two sub datasets, our method achieved adequate results, which can obtain 72.08% and 75.14% accuracy in across-subject experiment,67.71% and 61.42% accuracy in across-device experiment, showing the significance and potential of the wearable EEG monitoring application based on VR in real life situation.