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首页> 外文期刊>IEEE sensors journal >Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform From EEG Signals
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Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform From EEG Signals

机译：使用脑电信号的柔性解析小波变换进行跨主题情感识别

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摘要

Human emotion is a physical or psychological process which is triggered either consciously or unconsciously due to perception of any object or situation. The electroencephalogram (EEG) signals can be used to record ongoing neuronal activities in the brain to get the information about the human emotional state. These complicated neuronal activities in the brain cause non-stationary behavior of the EEG signals. Thus, emotion recognition using EEG signals is a challenging study and it requires advanced signal processing techniques to extract the hidden information of emotions from EEC signals. Due to poor generalizability of features from EEG signals across subjects, recognizing cross-subject emotion has been difficult. Thus, our aim is to comprehensively investigate the channel specific nature of EEG signals and to provide an effective method based on flexible analytic wavelet transform (FAWT) for recognition of emotion. FAWT decomposes the EEG signal into different sub-band signals. Furthermore, we applied information potential to extract the features from the decomposed sub-band signals of EEG signal. The extracted feature values were smoothed and fed to the random forest and support vector machine classifiers that classified the emotions. The proposed method is applied to two different publicly available databases which are SJTU emotion EEG dataset and database for emotion analysis using physiological signal. The proposed method has shown better performance for human emotion classification as compared to the existing method. Moreover, it yields channel specific subject classification of emotion EEG signals when exposed to the same stimuli.
机译：人类情感是由于感知到任何物体或情况而有意识或无意识地触发的生理或心理过程。脑电图（EEG）信号可用于记录大脑中正在进行的神经元活动，以获取有关人类情绪状态的信息。脑中这些复杂的神经元活动导致EEG信号的非平稳行为。因此，使用EEG信号进行情感识别是一项具有挑战性的研究，它需要先进的信号处理技术才能从EEC信号中提取情感的隐藏信息。由于跨受试者的EEG信号特征的通用性较差，因此很难识别跨主体的情感。因此，我们的目的是全面研究脑电信号的通道特异性，并提供一种基于柔性分析小波变换（FAWT）的有效情感识别方法。 FAWT将EEG信号分解为不同的子带信号。此外，我们利用信息势从脑电信号的分解子带信号中提取特征。提取的特征值经过平滑处理，然后输入到对情感进行分类的随机森林和支持向量机分类器中。所提出的方法被应用于两个不同的公开数据库，即SJTU情感脑电数据集和使用生理信号进行情感分析的数据库。与现有方法相比，该方法在人类情感分类中表现出更好的性能。此外，当暴露于相同刺激下时，它会产生情感脑电信号的特定于通道的主题分类。

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