  Electroencephalography (EEG) based emotion recognition has demonstrated
tremendous improvement in recent years. Specifically, numerous domain
adaptation (DA) algorithms have been exploited in the past five years to
enhance the generalization of emotion recognition models across subjects. The
DA methods assume that calibration data (although unlabeled) exists in the
target domain (new user). However, this assumption conflicts with the
application scenario that the model should be deployed without the
time-consuming calibration experiments. We argue that domain generalization
(DG) is more reasonable than DA in these applications. DG learns how to
generalize to unseen target domains by leveraging knowledge from multiple
source domains, which provides a new possibility to train general models. In
this paper, we for the first time benchmark state-of-the-art DG algorithms on
EEG-based emotion recognition. Since convolutional neural network (CNN), deep
brief network (DBN) and multilayer perceptron (MLP) have been proved to be
effective emotion recognition models, we use these three models as solid
baselines. Experimental results show that DG achieves an accuracy of up to
79.41\% on the SEED dataset for recognizing three emotions, indicting the
potential of DG in zero-training emotion recognition when multiple sources are
available.
