A Study on Facial Expression Change Detection Using Machine Learning Methods with Feature Selection Technique

Sang-Ha Sung, Sangjin Kim, Byung-Kwon Park, Do-Young Kang, Sunhae Sul, Jaehyun Jeong and Sung-Phil Kim
Additional contact information
Sang-Ha Sung: Department of Management Information Systems, Dong-A University, Busan 49236, Korea
Sangjin Kim: Department of Management Information Systems, Dong-A University, Busan 49236, Korea
Byung-Kwon Park: Department of Management Information Systems, Dong-A University, Busan 49236, Korea
Do-Young Kang: Department of Nuclear Medicine, Dong-A University, Busan 49236, Korea
Sunhae Sul: Department of Psychology, Pusan National University, Busan 46241, Korea
Jaehyun Jeong: Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
Sung-Phil Kim: Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea

Mathematics, 2021, vol. 9, issue 17, 1-16

Abstract: Along with the fourth industrial revolution, research in the biomedical engineering field is being actively conducted. Among these research fields, the brain–computer interface (BCI) research, which studies the direct interaction between the brain and external devices, is in the spotlight. However, in the case of electroencephalograph (EEG) data measured through BCI, there are a huge number of features, which can lead to many difficulties in analysis because of complex relationships between features. For this reason, research on BCIs using EEG data is often insufficient. Therefore, in this study, we develop the methodology for selecting features for a specific type of BCI that predicts whether a person correctly detects facial expression changes or not by classifying EEG-based features. We also investigate whether specific EEG features affect expression change detection. Various feature selection methods were used to check the influence of each feature on expression change detection, and the best combination was selected using several machine learning classification techniques. As a best result of the classification accuracy, 71% of accuracy was obtained with XGBoost using 52 features. EEG topography was confirmed using the selected major features, showing that the detection of changes in facial expression largely engages brain activity in the frontal regions.

Keywords: machine learning; classification; feature selection; BCI; EEG (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/17/2062/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/17/2062/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:17:p:2062-:d:622904

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().