Abstract
Bullying is an everlasting phenomenon and the first, yet difficult, step towards the solution is its detection. Conventional approaches for bullying incidence identification include questionnaires, conversations and psychological tests. Here, unlike the conventional approaches, two experiments are proposed that involve visual stimuli with cases of bullying- and non-bullying- related ones, set within a 2D (simple video preview) and a Virtual Reality (VR) (immersive video preview) context. In both experimental settings, brain activity is recorded using high density (HD) (256 channels) electroencephalogram (EEG), and analyzed to identify the bullying stimuli type (bullying/non-bullying) and context (2D/VR). The proposed classification analysis uses a convolutional neural network (CNN), applying deep learning on the oscillatory modes (OCMs) embedded within the raw HD EEG data. The extraction of OCMs from the HD EEG data is achieved with swarm decomposition (SWD), which efficiently accounts for the non-stationarity and noise contamination of the raw HD EEG data. Experimental results from 17 subjects indicate that the new SWD/CNN approach achieves high discrimination accuracy (AUC = 0.987 between bullying/non-bullying stimuli type; AUC = 0.975, between bullying/non-bullying stimuli type and 2D/VR context), paving the way for better understanding of how brain's responses could act as indicators of bullying experience within immersive environments.