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Kim, Sung-Phil
Brain-Computer Interface (BCI) Lab
Research Interests
  • Brain-computer interface, Statistical Signal Processing, Neural Code, Neuromarketing

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

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dc.contributor.author Sung, Sang-Ha ko
dc.contributor.author Kim, Sangjin ko
dc.contributor.author Park, Byung-Kwon ko
dc.contributor.author Kang, Do-Young ko
dc.contributor.author Sul, Sunhae ko
dc.contributor.author Jeong, Jaehyun ko
dc.contributor.author Kim, Sung-Phil ko
dc.date.available 2021-10-07T07:56:55Z -
dc.date.created 2021-10-01 ko
dc.date.issued 2021-09 ko
dc.identifier.citation MATHEMATICS, v.9, no.17, pp.2062 ko
dc.identifier.issn 2227-7390 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/54069 -
dc.description.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. ko
dc.language 영어 ko
dc.publisher MDPI ko
dc.title A Study on Facial Expression Change Detection Using Machine Learning Methods with Feature Selection Technique ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85114029365 ko
dc.identifier.wosid 000694269100001 ko
dc.type.rims ART ko
dc.identifier.doi 10.3390/math9172062 ko
dc.identifier.url https://www.mdpi.com/2227-7390/9/17/2062 ko
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