| Title |
Emotion Classification DNN Model for Virtual Reality based 3D Space |
| Authors |
Myung, Jee-Yeon ; Jun, Han-Jong |
| DOI |
https://doi.org/10.5659/JAIK_PD.2020.36.4.41 |
| Keywords |
Virtual Reality(VR); Emotion; Electroencephalography(EEG); Fast Fourier Transform(FFT); Deep Learning |
| Abstract |
The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user’s emotions, in particular
Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to
measure a user’s emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a
questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant
difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as
labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the
model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility
of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a
user’s emotions toward VR based 3D design alternatives by measuring the EEG with this model. |