Tavakkoli Hoda, Motie Nasrabadi Ali
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Front Hum Neurosci. 2022 Jun 29;16:936393. doi: 10.3389/fnhum.2022.936393. eCollection 2022.
Emotion recognition systems have been of interest to researchers for a long time. Improvement of brain-computer interface systems currently makes EEG-based emotion recognition more attractive. These systems try to develop strategies that are capable of recognizing emotions automatically. There are many approaches due to different features extractions methods for analyzing the EEG signals. Still, Since the brain is supposed to be a nonlinear dynamic system, it seems a nonlinear dynamic analysis tool may yield more convenient results. A novel approach in Symbolic Time Series Analysis (STSA) for signal phase space partitioning and symbol sequence generating is introduced in this study. Symbolic sequences have been produced by means of spherical partitioning of phase space; then, they have been compared and classified based on the maximum value of a similarity index. Obtaining the automatic independent emotion recognition EEG-based system has always been discussed because of the subject-dependent content of emotion. Here we introduce a subject-independent protocol to solve the generalization problem. To prove our method's effectiveness, we used the DEAP dataset, and we reached an accuracy of 98.44% for classifying happiness from sadness (two- emotion groups). It was 93.75% for three (happiness, sadness, and joy), 89.06% for four (happiness, sadness, joy, and terrible), and 85% for five emotional groups (happiness, sadness, joy, terrible and mellow). According to these results, it is evident that our subject-independent method is more accurate rather than many other methods in different studies. In addition, a subject-independent method has been proposed in this study, which is not considered in most of the studies in this field.
情感识别系统长期以来一直受到研究人员的关注。脑机接口系统的改进使得基于脑电图的情感识别更具吸引力。这些系统试图开发能够自动识别情感的策略。由于分析脑电图信号的特征提取方法不同，存在许多方法。然而，由于大脑被认为是一个非线性动态系统，非线性动态分析工具似乎可能产生更便利的结果。本研究介绍了一种符号时间序列分析（STSA）中用于信号相空间划分和符号序列生成的新方法。符号序列通过相空间的球形划分产生；然后，基于相似性指标的最大值对它们进行比较和分类。由于情感的个体依赖性内容，基于脑电图的自动独立情感识别系统一直是人们讨论的话题。在这里，我们引入一种独立于个体的协议来解决泛化问题。为了证明我们方法的有效性，我们使用了DEAP数据集，在区分快乐与悲伤（两组情感）时，分类准确率达到了98.44%。对于三种情感（快乐、悲伤和喜悦），准确率为93.75%；对于四种情感（快乐、悲伤、喜悦和恐惧），准确率为89.06%；对于五种情感组（快乐、悲伤、喜悦、恐惧和柔和），准确率为85%。根据这些结果，很明显我们的独立于个体的方法比不同研究中的许多其他方法更准确。此外，本研究还提出了一种独立于个体的方法，这在该领域的大多数研究中都未被考虑。
