In human contact, emotion is very crucial. Attributes like words, voice intonation, facial expressions, and kinesics can all be used to portray one's feelings. However, brain-computer interface (BCI) devices have not yet reached the level required for emotion interpretation. With the rapid development of machine learning algorithms, dry electrode techniques, and different real-world applications of the brain-computer interface for normal individuals, emotion categorization from EEG data has recently gotten a lot of attention. Electroencephalogram (EEG) signals are a critical resource for these systems. The primary benefit of employing EEG signals is that they reflect true emotion and are easily resolved by computer systems. In this work, EEG signals associated with good, neutral, and negative emotions were identified using channel selection preprocessing. However, researchers had a limited grasp of the specifics of the link between various emotional states until now. To identify EEG signals, we used discrete wavelet transform and machine learning techniques such as recurrent neural network (RNN) and k-nearest neighbor (kNN) algorithm. Initially, the classifier methods were utilized for channel selection. As a result, final feature vectors were created by integrating the features of EEG segments from these channels. Using the RNN and kNN algorithms, the final feature vectors with connected positive, neutral, and negative emotions were categorized independently. The classification performance of both techniques is computed and compared. Using RNN and kNN, the average overall accuracies were 94.844 % and 93.438 %, respectively.
翻译:在人类接触中,情感是非常关键的。 语言、 声音、 内向、 面部表达方式和动脉等属性都可用于描述一个人的情感。 但是, 大脑- 计算机界面( BCII) 设备尚未达到情感解释所需的水平 。 但是, 随着机器学习算法、 干电极技术的迅速发展, 以及大脑- 计算机界面对正常人的不同真实世界应用, EEEG 数据中的情感分类最近引起了很大的注意。 电脑图信号是这些系统的关键资源。 使用 EEEEG 信号的主要好处是它们反映真实的情感,并且很容易由计算机系统解决。 在这项工作中, 与良好、 中性和负性情感相关的 EEEG 信号尚未达到所需的水平。 然而, 研究人员对各种情绪状态之间联系的具体特点了解有限。 为了识别 EEEG 信号, 我们使用了离散波变和机器学习技术, 如循环神经网络( RNNN) 和 KNNN 邻居( kNNN) 算法 。 最初, 的分类方法是用于频道选择中、 RNNNE 和 CL 等 等 等 特性的最后特性,, 和矢变变变换成为正性 。