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

Abstract

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.

Neural Correlates of Preference: A Transmodal Validation Study

“…The results for the adventure video are coherent with the literature on affective reactions induced by videos (Schellberg et al, 1990 ; Vecchiato et al, 2010 , 2014 ; Koelstra et al, 2012 ; Kortelainen et al, 2015 ; Güzel Aydin et al, 2016 ), especially in terms of variables that represent valence and arousal, except for beta in Fp2 (Koelstra et al, 2012 ), indicating that these variables are associated with liking encoding in this thematic category, at least from the indicators that have already been mapped. In contrast, only six of 17 EEG variables in the comedy video presented significant results that were consistent with the literature in affective reactions induced by videos, more specifically: theta in F1, alpha in P4, alpha and gamma in F2, beta in F2 and P3, being the first four variables mainly involved in valence processing (Schellberg et al, 1990 ; Vecchiato et al, 2010 ; Silberstein and Nield, 2008 ; Vecchiato et al, 2014 ; Koelstra et al, 2012 ; Kortelainen et al, 2015 ) and the final two involved in the processing of both valence and arousal dimensions (Koelstra et al, 2012 ; Kortelainen et al, 2015 ).…”

Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition

“…The second approach defines emotion as a continuous 4-D space of valence, arousal, dominance and liking [21,22]. In most of the studies, this space is reduced to 2-D as valence and arousal dimensions [23,24]. The study conducted in [25] is very useful in the sense that it relates discrete and continuous approaches to each other.…”

“…In EEG data channels, typical frequency domain analysis is used. In the frequency domain, the most important frequency bands are delta (1-3 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12)(13), beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50) [26]. Fast Fourier Transform (FFT), Wavelet Transform (WT), eigenvector and autoregressive are the methods which transform EEG signal from time domain to frequency domain [27].…”

Learning with computers: Generating insights into the development of cognitive tools using cultural historical activity theory

“…Practically, neurotechnology, such as electroencephalography (EEG) devices, has been shown to be promising in assessing emotions by analysing the frequency bands of gamma, beta, alpha, theta, and delta (Hu et al, 2017). Researchers have found the combination of gamma and alpha bands to be sensitive to valence and the arousal of emotions (Guzel Aydin, Kaya, & Guler, 2016). EEG signals can thus be used to collect emotions data from students.…”