As the key to realizing aBCIs, EEG emotion recognition has been widely
studied by many researchers. Previous methods have performed well for
intra-subject EEG emotion recognition. However, the style mismatch between
source domain (training data) and target domain (test data) EEG samples caused
by huge inter-domain differences is still a critical problem for EEG emotion
recognition. To solve the problem of cross-dataset EEG emotion recognition, in
this paper, we propose an EEG-based Emotion Style Transfer Network (E2STN) to
obtain EEG representations that contain the content information of source
domain and the style information of target domain, which is called stylized
emotional EEG representations. The representations are helpful for
cross-dataset discriminative prediction. Concretely, E2STN consists of three
modules, i.e., transfer module, transfer evaluation module, and discriminative
prediction module. The transfer module encodes the domain-specific information
of source and target domains and then re-constructs the source domain's
emotional pattern and the target domain's statistical characteristics into the
new stylized EEG representations. In this process, the transfer evaluation
module is adopted to constrain the generated representations that can more
precisely fuse two kinds of complementary information from source and target
domains and avoid distorting. Finally, the generated stylized EEG
representations are fed into the discriminative prediction module for final
classification. Extensive experiments show that the E2STN can achieve the
state-of-the-art performance on cross-dataset EEG emotion recognition tasks.

本研究提出了一种基于脑电图情绪风格转移网络（E2STN）的方法，用于解决脑电图情绪识别中源领域和目标领域之间巨大差异导致的跨数据集问题，并且在大量实验中表明，E2STN 在跨数据集脑电图情绪识别任务中实现了最先进的性能。