Jiangsu Vocational College of Information Technology, Wuxi, Jiangsu 214153, China.
Jiangsu Key Laboratory of Media Design and Software Technology (Jiangnan University), Wuxi 214122, China.
Comput Intell Neurosci. 2022 May 26;2022:1343358. doi: 10.1155/2022/1343358. eCollection 2022.
With the increasing pressure on college students in terms of study, work, emotion, and life, the emotional changes of college students are becoming more and more obvious. For college student management workers, if they can accurately grasp the emotional state of each college student in all aspects of the whole process, it will be of great help to student management work. The traditional way to understand students' emotions at a certain stage is mostly through chats, questionnaires, and other methods. However, data collection in this way is time-consuming and labor-intensive, and the authenticity of the collected data cannot be guaranteed because students will lie out of impatience or unwillingness to reveal their true emotions. In order to explore an accurate and efficient emotion recognition method for college students, more objective physiological data are used for emotion recognition research. Since emotion is generated by the central nervous system of the human brain, EEG signals directly reflect the electrophysiological activity of the brain. Therefore, in the field of emotion recognition based on physiological signals, EEG signals are favored due to their ability to intuitively respond to emotions. Therefore, a deep neural network (DNN) is used to classify the collected emotional EEG data and obtain the emotional state of college students according to the classification results. Considering that different features can represent different information of the original data, in order to express the original EEG data information as comprehensively as possible, various features of the EEG are first extracted. Second, feature fusion is performed on multiple features using the autosklearn model integration technique. Third, the fused features are input to the DNN, resulting in the final classification result. The experimental results show that the method has certain advantages in public datasets, and the accuracy of emotion recognition exceeds 88%. This proves the used emotion recognition is feasible to be applied in real life.
随着大学生在学习、工作、情感和生活方面压力的不断增加，大学生的情绪变化也越来越明显。对于大学生管理工作者来说，如果能够准确掌握每个大学生在整个过程中各个方面的情绪状态，将对学生管理工作有很大的帮助。传统上了解学生某个阶段情绪的方法主要是通过聊天、问卷调查等方式。然而，这种方式的数据收集既费时又费力，而且由于学生出于不耐烦或不愿意透露真实情绪而撒谎，收集到的数据的真实性无法保证。为了探索一种准确高效的大学生情绪识别方法，更多客观的生理数据被用于情绪识别研究。由于情绪是由人体大脑的中枢神经系统产生的，EEG 信号直接反映了大脑的电生理活动。因此，在基于生理信号的情绪识别领域，EEG 信号由于能够直观地响应情绪而受到青睐。因此，使用深度神经网络（DNN）对收集到的情绪 EEG 数据进行分类，并根据分类结果获得大学生的情绪状态。考虑到不同的特征可以表示原始数据的不同信息，为了尽可能全面地表达原始 EEG 数据信息，首先提取 EEG 的各种特征。其次，使用 autosklearn 模型集成技术对多个特征进行特征融合。然后，将融合后的特征输入 DNN，得出最终的分类结果。实验结果表明，该方法在公共数据集上具有一定的优势，情绪识别的准确率超过 88%。这证明了所使用的情绪识别方法在实际生活中是可行的。
