This paper illustrates the wavelet-based feature extraction for emotion assessment using electroencephalogram (EEG) signal through graphical coding design. Two-dimensional (valence-arousal) emotion model was studied. Different emotions (happy, joy, melancholy, and disgust) were studied for assessment. These emotions were stimulated by video clips. EEG signals obtained from four subjects were decomposed into five frequency bands (gamma, beta, alpha, theta, and delta) using "db5" wavelet function. Relative features were calculated to obtain further information. Impact of the emotions according to valence value was observed to be optimal on power spectral density of gamma band. The main objective of this work is not only to investigate the influence of the emotions on different frequency bands but also to overcome the difficulties in the text-based program. This work offers an alternative approach for emotion evaluation through EEG processing. There are a number of methods for emotion recognition such as wavelet transform-based, Fourier transform-based, and Hilbert-Huang transform-based methods. However, the majority of these methods have been applied with the text-based programming languages. In this study, we proposed and implemented an experimental feature extraction with graphics-based language, which provides great convenience in bioelectrical signal processing.

中文翻译：

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使用 LabVIEW 对 EEG 信号情绪的价唤醒模型进行基于小波的研究。


本文通过图形编码设计说明了使用脑电图（EEG）信号进行基于小波的特征提取以进行情绪评估。研究了二维（价-唤醒）情绪模型。研究了不同的情绪（快乐、喜悦、忧郁和厌恶）进行评估。这些情绪是由视频片段激发的。使用“db5”小波函数将从四名受试者获得的脑电图信号分解为五个频带（gamma、beta、alpha、theta 和 delta）。计算相关特征以获得进一步的信息。据观察，根据价值的情绪影响对伽马带的功率谱密度是最佳的。这项工作的主要目的不仅是研究情绪对不同频段的影响，而且要克服基于文本的程序中的困难。这项工作提供了一种通过脑电图处理进行情绪评估的替代方法。情感识别有多种方法，例如基于小波变换的方法、基于傅里叶变换的方法和基于希尔伯特-黄变换的方法。然而，这些方法中的大多数已经应用于基于文本的编程语言。在本研究中，我们提出并实现了一种基于图形语言的实验性特征提取，为生物电信号处理提供了极大的便利。