Neural correlates of affective content: application to perceptual tagging of video

Affective Video Tagging Framework using Human Attention Modelling through EEG Signals

“…Further, various regions of the human brain respond differently with respect to the task they are performing. The involvement of different brain regions is described below (Müller, 2008;(Ghaemmaghami, 2017;Sharma et al, 2021).…”

“…Whenever the brain is in a certain state it generates a specific pattern of an electrical signal, EEG recording gives variable frequency patterns change, and analysis of those patterns also gives insight into different cognitive processes, (Sharma et al, 2021;Gawali et al, 2012;Alarcao & Fonseca, 2019). The significance of specific frequency ranges is presented in Table 1.…”

“…Motivated by the applicability of EEG signals and the role of affectiveness in multimedia content assessment, in this paper, the work is presented towards proposing a model for automatic tagging of videos by modeling the human's EEG responses corresponding to specific video content. The work presented here is an extension of the previously published work done by Sharma et al (2021), where the authors explored the relationship between affective content and corresponding EEG responses of viewers. Different brain sections like Frontal, Temporal, Parietal, and Occipital parts have been explored using extracted frequency bands of EEG signals i.e., Alpha, Beta, Gamma, Delta, and Theta.…”

“…Different brain sections like Frontal, Temporal, Parietal, and Occipital parts have been explored using extracted frequency bands of EEG signals i.e., Alpha, Beta, Gamma, Delta, and Theta. In continuation of this, here an automatic video tagging model is developed using the analysis presented in Sharma et al (2021). The significant contributions of the presented research are mentioned as follows:…”

“…• Further, to characterize the factors that drive the viewer's attention and affective state, the combined effect at different brain locations is used to model human attention. The analysis presented in Sharma et al (2021) is used to formulate attention and affect-based features. • In the last, the extracted features were used to model the automatic tagging of two categories of videos using SVM-based classifiers.…”