Su Jipu, Zhu Jie, Song Tiecheng, Chang Hongli
School of Information Science and Engineering, Southeast University, Nanjing 210096, China.
Brain Sci. 2023 Jun 21;13(7):977. doi: 10.3390/brainsci13070977.
One of the primary challenges in Electroencephalogram (EEG) emotion recognition lies in developing models that can effectively generalize to new unseen subjects, considering the significant variability in EEG signals across individuals. To address the issue of subject-specific features, a suitable approach is to employ projection dictionary learning, which enables the identification of emotion-relevant features across different subjects. To accomplish the objective of pattern representation and discrimination for subject-independent EEG emotion recognition, we utilized the fast and efficient projection dictionary pair learning (PDPL) technique. PDPL involves the joint use of a synthesis dictionary and an analysis dictionary to enhance the representation of features. Additionally, to optimize the parameters of PDPL, which depend on experience, we applied the genetic algorithm (GA) to obtain the optimal solution for the model. We validated the effectiveness of our algorithm using leave-one-subject-out cross validation on three EEG emotion databases: SEED, MPED, and GAMEEMO. Our approach outperformed traditional machine learning methods, achieving an average accuracy of 69.89% on the SEED database, 24.11% on the MPED database, 64.34% for the two-class GAMEEMO, and 49.01% for the four-class GAMEEMO. These results highlight the potential of subject-independent EEG emotion recognition algorithms in the development of intelligent systems capable of recognizing and responding to human emotions in real-world scenarios.
脑电图（EEG）情感识别的主要挑战之一在于开发能够有效推广到未见过的新受试者的模型，因为个体间的EEG信号存在显著差异。为了解决受试者特定特征的问题，一种合适的方法是采用投影字典学习，它能够识别不同受试者之间与情感相关的特征。为了实现独立于受试者的EEG情感识别的模式表示和区分目标，我们使用了快速高效的投影字典对学习（PDPL）技术。PDPL涉及联合使用合成字典和分析字典来增强特征表示。此外，为了优化依赖经验的PDPL参数，我们应用遗传算法（GA）来获得模型的最优解。我们在三个EEG情感数据库（SEED、MPED和GAMEEMO）上使用留一受试者交叉验证来验证我们算法的有效性。我们的方法优于传统机器学习方法，在SEED数据库上的平均准确率为69.89%，在MPED数据库上为24.11%，在两类GAMEEMO上为64.34%，在四类GAMEEMO上为49.01%。这些结果凸显了独立于受试者的EEG情感识别算法在开发能够在现实场景中识别和响应人类情感的智能系统方面的潜力。
