Preliminary test of affective virtual reality scenes with head mount display for emotion elicitation experiment

Abstract

Emotion elicitation experiments are conducted to collect biological signals from a subject who is in a state of emotion. The recorded signals are used as training/test dataset for constructing an emotion recognition system by means of machine learning. In conventional emotion elicitation experiments, affective images or videos were provided for a subject to draw out an emotion from them. However, the authors have concerns about the effectiveness. To surely evoke a specific emotion from subjects, we have produced several Virtual Reality (VR) scenes and provided the subjects with the scenes through a Head Mount Display (HMD) in emotion elicitation experiments. Usability and effectiveness of the VR scenes with the HMD for emotion elicitation were experimentally verified. It was confirmed that experience of the VR scenes with the HMD was effective in evoking emotions, but we have to improve how subjects learn a way of playing VR scenes and provide measures against VR sickness at any cost. Moreover, Support Vector Machine classifiers as an emotion recognition system were constructed using the biological signals measured from the subjects in the emotion elicitation experiments.

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

“…Of these, the best performing classifier for intrasubject classification was achieved by RF (98.20%) by Kumaran et al [ 93 ] on music stimuli while the best for intersubject classification was achieved by DGCNN (90.40%) by Song et al [ 110 ] using video stimulations from SEED and DREAMER datasets. As for VR stimuli, only Hidaka et al [ 116 ] performed using SVM (81.33%) but using only five subjects to evaluate its performance, which is considered to be very low when the number of subjects at minimal is expected to be 30 to be justifiable as mentioned by Alarcao and Fonseca [ 22 ].…”

Co-Design with Myself: A Brain-Computer Interface Design Tool that Predicts Live Emotion to Enhance Metacognitive Monitoring of Designers

“…Particularly focused on arousal and valence affective response, previous studies have used the EEG classification approach in the context of using music stimuli [66], music videos [6,16,83,96,100,101], and video clips [48,56,58,68,88,93]. The immersive virtual reality (VR)'s ability to evoke emotion gives VR more interest to be used as a tool in emotion detection in general [28,33,35,36,47,105]. In fact, the self-reported intensity of emotion was found to be significantly greater in immersive VR compared to similar content in non-immersive virtual environments [10].…”

EEG-Based Emotion Recognition Using Deep Learning and M3GP

“…Neural Network [31][32][33] 85.80% Support Vector Machine [34][35][36] 77.80% K-Nearest Neighbor [33,37,38] 88.94% Multi-layer Perceptron [38][39][40] 78.16% Bayes [41][42][43] 69.62% Extreme Learning Machine [41] 87.10% K-Means [43] 78.06% Linear Discriminant Analysis [42] 71.30% Gaussian Process [44] 71.30% To improve on the performance of the SOA methods, we used the features generated by M3GP in Figure 15. This kind of transfer learning was used in [45] with success and the best training transformation found in M3GP was used to transform the dataset into a new one (M3GP tree in Section 4.2), considering that these new features contain more information to simplify the learning process of the SOA methods.…”