Wavelet-based study of valence–arousal model of emotions on EEG signals with LabVIEW

• Published in: Brain informatics Vol. 3; no. 2; pp. 109 - 117
• Main Authors: Guzel Aydin, Seda, Kaya, Turgay, Guler, Hasan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2016; Springer Nature B.V
• Subjects: Arousal; Artificial Intelligence; Bioelectricity; Cognitive Psychology; Computation by Abstract Devices; Computer Science; Electroencephalography; Emotion recognition; Emotions; Feature extraction; Fourier transforms; Frequencies; Health Informatics; Neurosciences; Programming languages; Signal processing; Two dimensional models; Wavelet transforms; EEG; Emotions; LabVIEW
• ISSN: 2198-4018; 2198-4026; 2198-4018
• DOI: 10.1007/s40708-016-0031-9

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.