School of Software, South China Normal University, Guangzhou 510631, China.
School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China.
Sensors (Basel). 2020 May 27;20(11):3028. doi: 10.3390/s20113028.
：Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects' emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.
脑电（EEG）信号已被广泛应用于情感识别。然而，目前基于 EEG 的情感识别在情感分类的准确性上较低，其实时应用受到限制。为了解决这些问题，在本文中，我们提出了一种改进的特征选择算法，基于 EEG 信号来识别被试的情绪状态，并结合这种特征选择方法设计了一个在线情感识别脑机接口（BCI）系统。具体来说，首先，从时域、频域和时频域提取不同维度的特征。然后，提出了一种带有多阶段线性递减惯性权重（MLDW）的改进粒子群优化（PSO）方法进行特征选择。MLDW 算法可用于轻松细化惯性权重递减的过程。最后，使用支持向量机分类器对情绪类型进行分类。我们从由 32 名被试采集的 DEAP 数据集的 EEG 数据中提取不同的特征，进行了两个离线实验。结果表明，四类情感识别的平均准确率达到了 76.67%。与最新的基准相比，我们提出的 MLDW-PSO 特征选择方法提高了基于 EEG 的情感识别的准确性。为了进一步验证 MLDW-PSO 特征选择方法的效率，我们开发了一个由中文视频引发的在线两分类情感识别系统，10 名健康被试的平均准确率达到了 89.5%，取得了良好的性能。因此，证明了我们方法的有效性。
