Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
J Healthc Eng. 2023 Jan 19;2023:9223599. doi: 10.1155/2023/9223599. eCollection 2023.
Emotion recognition based on brain signals has increasingly become attractive to evaluate human's internal emotional states. Conventional emotion recognition studies focus on developing machine learning and classifiers. However, most of these methods do not provide information on the involvement of different areas of the brain in emotions. Brain mapping is considered as one of the most distinguishing methods of showing the involvement of different areas of the brain in performing an activity. Most mapping techniques rely on projection and visualization of only one of the electroencephalogram (EEG) subband features onto brain regions. The present study aims to develop a new EEG-based brain mapping, which combines several features to provide more complete and useful information on a single map instead of common maps. In this study, the optimal combination of EEG features for each channel was extracted using a stacked autoencoder (SAE) network and visualizing a topographic map. Based on the research hypothesis, autoencoders can extract optimal features for quantitative EEG (QEEG) brain mapping. The DEAP EEG database was employed to extract topographic maps. The accuracy of image classifiers using the convolutional neural network (CNN) was used as a criterion for evaluating the distinction of the obtained maps from a stacked autoencoder topographic map (SAETM) method for different emotions. The average classification accuracy was obtained 0.8173 and 0.8037 in the valence and arousal dimensions, respectively. The extracted maps were also ranked by a team of experts compared to common maps. The results of quantitative and qualitative evaluation showed that the obtained map by SAETM has more information than conventional maps.
基于脑信号的情绪识别越来越受到关注，用于评估人类的内部情绪状态。传统的情绪识别研究侧重于开发机器学习和分类器。然而，这些方法大多没有提供关于大脑不同区域参与情绪的信息。脑映射被认为是展示大脑不同区域参与执行活动的一种最具区别性的方法。大多数映射技术依赖于将脑电图（EEG）子带特征中的仅一个投影和可视化到大脑区域。本研究旨在开发一种新的基于 EEG 的脑映射，该方法结合了多个特征，以在单个地图上提供更完整和有用的信息，而不是常见的地图。在这项研究中，使用堆叠自动编码器（SAE）网络提取每个通道的 EEG 特征的最佳组合，并可视化地形图。基于研究假设，自动编码器可以提取定量脑电图（QEEG）脑映射的最佳特征。使用卷积神经网络（CNN）的图像分类器的准确性被用作评估从堆叠自动编码器地形图（SAETM）方法获得的地图与不同情绪之间的区别的标准。在效价和唤醒度维度上，平均分类准确性分别为 0.8173 和 0.8037。提取的地图还由一组专家进行了排名，与常见地图进行了比较。定量和定性评估的结果表明，SAETM 获得的地图比传统地图具有更多的信息。
