  The integration of human emotions into multimedia applications shows great
potential for enriching user experiences and enhancing engagement across
various digital platforms. Unlike traditional methods such as questionnaires,
facial expressions, and voice analysis, brain signals offer a more direct and
objective understanding of emotional states. However, in the field of
electroencephalography (EEG)-based emotion recognition, previous studies have
primarily concentrated on training and testing EEG models within a single
dataset, overlooking the variability across different datasets. This oversight
leads to significant performance degradation when applying EEG models to
cross-corpus scenarios. In this study, we propose a novel Joint Contrastive
learning framework with Feature Alignment (JCFA) to address cross-corpus
EEG-based emotion recognition. The JCFA model operates in two main stages. In
the pre-training stage, a joint domain contrastive learning strategy is
introduced to characterize generalizable time-frequency representations of EEG
signals, without the use of labeled data. It extracts robust time-based and
frequency-based embeddings for each EEG sample, and then aligns them within a
shared latent time-frequency space. In the fine-tuning stage, JCFA is refined
in conjunction with downstream tasks, where the structural connections among
brain electrodes are considered. The model capability could be further enhanced
for the application in emotion detection and interpretation. Extensive
experimental results on two well-recognized emotional datasets show that the
proposed JCFA model achieves state-of-the-art (SOTA) performance, outperforming
the second-best method by an average accuracy increase of 4.09% in cross-corpus
EEG-based emotion recognition tasks.
