Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors
Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors
复制标题
人脑脑电图传感器情绪识别的最优特征选择和深度学习集成方法
DOI：
10.1109/access.2017.2724555
复制
发表时间：
2017-01-01
期刊：
影响因子：
3.9
通讯作者：
Lee, Hyo Jong
中科院分区：
文献类型：
--
作者：
Mehmood, Raja Majid;Du, Ruoyu;Lee, Hyo Jong
人机交互研究的最新进展使神经精神障碍或残疾患者通过脑机接口系统进行情感交流成为可能。本文通过分析非侵入性测量人脑内部神经元电活动的脑电传感器产生的脑电信号的特征，并选择这些特征的最佳组合进行识别，从而有效地识别情绪状态。本文采用14导联脑电图机记录了21名12~14岁健康受试者的头皮脑电数据，同时观察了4种情绪刺激(高兴、平静、悲伤、恐惧)的脑电图像。经过预处理后，使用Hjorth参数(活跃度、迁移率和复杂性)来衡量时间序列数据的信号活跃度。在计算了不同频段的Hjorth参数后，采用平衡的单因素方差分析选择了最优的脑电特征。通过这种统计方法选择的特征优于单变量和多变量特征。使用支持向量机、k近邻、线性判别分析、朴素贝叶斯、随机森林、深度学习和四种集成方法(装袋、增强、堆叠和投票)对最优特征进行进一步的情感分类。实验结果表明，与常用的频谱功率带方法相比，该方法显著提高了情感识别率。
Recent advancements in human computer interaction research have led to the possibility of emotional communication via brain computer interface systems for patients with neuropsychiatric disorders or disabilities. In this paper, we efficiently recognize emotional states by analyzing the features of electroencephalography (EEG) signals, which are generated from EEG sensors that noninvasively measure the electrical activity of neurons inside the human brain, and select the optimal combination of these features for recognition. In this paper, the scalp EEG data of 21 healthy subjects (12-14 years old) were recorded using a 14-channel EEG machine while the subjects watched images with four types of emotional stimuli (happy, calm, sad, or scared). After preprocessing, the Hjorth parameters (activity, mobility, and complexity) were used to measure the signal activity of the time series data. We selected the optimal EEG features using a balanced one-way ANOVA after calculating the Hjorth parameters for different frequency ranges. Features selected by this statistical method outperformed univariate and multivariate features. The optimal features were further processed for emotion classification using support vector machine, k-nearest neighbor, linear discriminant analysis, Naive Bayes, random forest, deep learning, and four ensembles methods (bagging, boosting, stacking, and voting). The results show that the proposed method substantially improves the emotion recognition rate with respect to the commonly used spectral power band method.