Goshvarpour Atefeh, Goshvarpour Ateke
Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan Iran.
Cogn Neurodyn. 2024 Jun;18(3):1061-1077. doi: 10.1007/s11571-023-09968-6. Epub 2023 Apr 23.
Thanks to the advent of affective computing, designing an automatic human emotion recognition system for clinical and non-clinical applications has attracted the attention of many researchers. Currently, multi-channel electroencephalogram (EEG)-based emotion recognition is a fundamental but challenging issue. This experiment envisioned developing a new scheme for automated EEG affect recognition. An innovative nonlinear feature engineering approach was presented based on Lemniscate of Bernoulli's Map (LBM), which belongs to the family of chaotic maps, in line with the EEG's nonlinear nature. As far as the authors know, LBM has not been utilized for biological signal analysis. Next, the map was characterized using several graphical indices. The feature vector was imposed on the feature selection algorithm while evaluating the role of the feature vector dimension on emotion recognition rates. Finally, the efficiency of the features on emotion recognition was appraised using two conventional classifiers and validated using the Database for Emotion Analysis using Physiological signals (DEAP) and SJTU Emotion EEG Dataset-IV (SEED-IV) benchmark databases. The experimental results showed a maximum accuracy of 92.16% for DEAP and 90.7% for SEED-IV. Achieving higher recognition rates compared to the state-of-art EEG emotion recognition systems suggest the proposed method based on LBM could have potential both in characterizing bio-signal dynamics and detecting affect-deficit disorders.
得益于情感计算的出现，设计一个用于临床和非临床应用的自动人类情感识别系统已吸引了众多研究人员的关注。当前，基于多通道脑电图（EEG）的情感识别是一个基本但具有挑战性的问题。本实验设想开发一种用于自动EEG情感识别的新方案。基于伯努利双纽线映射（LBM）提出了一种创新的非线性特征工程方法，LBM属于混沌映射家族，符合EEG的非线性特性。据作者所知，LBM尚未用于生物信号分析。接下来，使用几个图形指标对该映射进行表征。在评估特征向量维度对情感识别率的作用时，将特征向量应用于特征选择算法。最后，使用两个传统分类器评估这些特征在情感识别上的效率，并使用生理信号情感分析数据库（DEAP）和上海交通大学情感EEG数据集-IV（SEED-IV）基准数据库进行验证。实验结果表明，DEAP的最高准确率为92.16%，SEED-IV的最高准确率为90.7%。与现有EEG情感识别系统相比实现了更高的识别率，这表明所提出的基于LBM的方法在表征生物信号动态和检测情感缺陷障碍方面都具有潜力。
