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.

本研究提出了一种新颖的联合对比学习框架与特征对齐模型（JCFA Model），用于解决跨语料库的基于脑电图（EEG）的情感识别问题。在两个主要阶段中，JCFA Model 通过联合领域对比学习策略，在无需标记数据的情况下，提取每个 EEG 样本的稳健的基于时间和频率的嵌入，并将其在共享的潜在时间 - 频率空间中进行对齐。随后，在与下游任务结合时，通过考虑脑电图电极之间的结构连接，进一步提高了模型的能力，适用于情感的检测和解读。大量的实验结果表明，所提出的 JCFA Model 在两个公认的情感数据集上实现了最先进的性能，其在跨语料库的 EEG 情感识别任务中的平均准确率提高了 4.09%。