| Title |
An EEG-based Deep Neural Network Classification Model for Recognizing Emotion of Users in Early Phase of Design |
| Authors |
Chang, Sun-Woo ; Dong, Won-Hyeok ; Jun, Han-Jong |
| DOI |
https://doi.org/10.5659/JAIK_PD.2018.34.12.85 |
| Keywords |
Affection Recognition ; Electroencephalography(EEG) ; Deep Neural Network Model ; TensorFlow |
| Abstract |
The purpose of this paper was to propose a model that recognizes potential users' emotional response toward design by classifying Electroencephalography(EEG). Studies in neuroscience and psychology have made an effort to recognize subjects' emotional response by analyzing EEG data. And this approach has been adopted in design since it is critical to monitor users' subjective response in the preface of design. Moreover, the building design process cannot be reversed after construction, recognizing clients' affection toward design alternatives plays important role. An experiment was conducted to record subjects' EEG data while they view their most/least liked images of small-house designs selected by them among the eight given images. After the recording, a subjective questionnaire, PANAS, was distributed to the subjects in order to describe their own affection score in quantitative way. Google TensorFlow was used to build and train the model. Dataset for model training and testing consist of feature columns for recorded EEG data and labels for the questionnaire results. After training and testing, the measured accuracy of the model was 0.975 which was higher than the other machine learning based classification methods. The proposed model may suggest one quantitative way of evaluating design alternatives. In addition, this method may support designer while designing the facilities for people like disabled or children who are not able to express their own feelings toward alternatives. |