Design of User Concentration Classification Model by EEG Analysis Based on Visual SCPT (시각 자극 기반의 뇌파 분석을 통한 사용자 집중력 분류 모델 설계)

Jinhyeok Park (박진혁); KANGSEOKHWAN (강석환); Byung Mun Lee (이병문); Un Gu Kang (강운구); Youngho Lee (이영호)

doi:10.9708/jksci.2018.23.11.129

Design of User Concentration Classification Model by EEG Analysis Based on Visual SCPT

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2018, 23(11), pp.129-135
DOI : 10.9708/jksci.2018.23.11.129
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : May 23, 2018
Accepted : October 25, 2018
Published : November 30, 2018

Jinhyeok Park ¹, KANGSEOKHWAN ¹, Byung Mun Lee ¹, Un Gu Kang ¹, Youngho Lee ¹

¹가천대학교

Accredited

ABSTRACT

In this study, we designed a model that can measure the level of user's concentration by measuring and analyzing EEG data of the subjects who are performing Continuous Performance Test based on visual stimulus. This study focused on alpha and beta waves, which are closely related to concentration in various brain waves. There are a lot of research and services to enhance not only concentration but also brain activity. However, there are formidable barriers to ordinary people for using routinely because of high cost and complex procedures. Therefore, this study designed the model using the portable EEG measurement device with reasonable cost and Visual Continuous Performance Test which we developed as a simplified version of the existing CPT. This study aims to measure the concentration level of the subject objectively through simple and affordable way, EEG analysis. Concentration is also closely related to various brain diseases such as dementia, depression, and ADHD. Therefore, we believe that our proposed model can be useful not only for improving concentration but also brain disease prediction and monitoring research. In addition, the combination of this model and the Brain Computer Interface technology can create greater synergy in various fields.

KEYWORDS

EEG, Attention, Concentration, CPT, Visual SCPT

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* References for papers published after 2023 are currently being built.

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[confproc] In-Soon Park / 2003 / Optimization of brain function using neurofeedback – Focused on Clinical case / Proceedings of Korean Jungshin Science Symposium 18 : 64~85

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[report] Jong-Hun Jung / 2011 / A Study on EEG Signal Analysis using PCA and LDA / Institute of Control, Robotics and Systems : 708~711

[confproc] Hee-Won Lee / 2016 / A Study on the Electroencephalogram(EEG)Changes of Luminous Intensity and Color Temperature due to Attention / Korea Color Society Conference : 80~83

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[journal] 김종하 / 2016 / A Study on the Attention Concentration Properties in Convergent Exploration Situations in Cafe Space / 한국실내디자인학회 논문집 / 한국실내디자인학회 25(2) : 30~40

[journal] 박성수 / 2010 / A Study on Physiological Signal Changes Due to Distraction in Simulated Driving / 대한인간공학회지 / 대한인간공학회 29(1) : 55~59

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Design of User Concentration Classification Model by EEG Analysis Based on Visual SCPT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Design of User Concentration Classification Model by EEG Analysis Based on Visual SCPT

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KEYWORDS

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* References for papers published after 2023 are currently being built.