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A Long Short Term Memory Deep Learning Network for the Classification of Negative Emotions Using EEG Signals

机译：使用脑电信号对负性情绪进行分类的长期短期记忆深度学习网络

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摘要

In cognitive science and human-computer interaction, automatic human emotion recognition using physiological stimuli is a key technology. This research considers classification of negative emotions using EEG signals in response to emotional clips. This paper introduces a long short term memory deep learning (LSTM) network to recognize emotions using EEG signals. The primary goal of this approach is to assess the classification performance of the LSTM model for classifying emotions. The secondary goal is to assess the human behavior of different age groups and genders. We have compared the performance of Multilayer Perceptron (MLP), K-nearest neighbors (KNN), Support Vector Machine (SVM), Deep Belief Network based SVM (DBN-SVM), and LSTM based deep learning model for classification of negative emotions using brain signals. The analysis shows that for four class of negative emotion recognition LSTM based deep learning model provides classification accuracy as 81.63%, 84.64%, 89.73%, and 92.84% for 50-50, 60-40, 70-30, and 10-fold cross-validation. Generalizability and reliability of this approach is evaluated by applying our approach to publicly available EEG dataset DEAP and SEED. In compliance with the self-reported feelings, brain signals of 26-35 years of age group provided the highest emotional identification. Among genders, females are more emotionally active as compared to males.
机译：在认知科学和人机互动中，使用生理刺激的自动人类情感识别是一个关键技术。本研究考虑使用EEG信号响应情绪剪辑的负面情绪的分类。本文介绍了长期短期内存深度学习（LSTM）网络，以识别使用EEG信号的情绪。这种方法的主要目标是评估LSTM模型的分类性能以进行分类情绪。二次目标是评估不同年龄组和性别的人类行为。我们已经比较了多层的Perceptron（MLP），K-CORMITY邻居（KNN），支持向量机（SVM），基于DBN-SVM的SVM（DBN-SVM）和基于LSTM的深度学习模型的性能，以便使用负面情绪的分类脑信号。分析表明，对于四类负情绪识别LSTM基于深度学习模型，提供分类精度为81.63％，84.64％，89.73％，92.84％，50-50,60-40,70-30和10倍十字-验证。通过将我们的方法应用于公开的EEG数据集DEAP和种子来评估这种方法的普遍性和可靠性。符合自我报告的感受，26-35岁的大脑信号提供了最高的情感识别。在树枝中，与男性相比，女性更具情感活跃。

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