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학술저널
저자정보
전형석 (서울대학교) 이준희 (서울대학교) 이준 (서울대학교) 황유진 (서울대학교) 이시영 (서울대학교) 양희 (서울대학교) 윤정한 (서울대학교) 권준수 (서울대학교) 원중호 (서울대학교) 조준동 (서울대학교) 이기원 (서울대학교)
저널정보
한국교원대학교 뇌기반교육연구소 Brain, Digital, & Learning Brain, Digital, & Learning Vol.9 No.3
발행연도
2019.1
수록면
105 - 112 (8page)

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Good performance is important element not only in workplace but also in daily activities. Performance of the human depends on the mental capacity and mental workload. Especially, children in concrete operational stage is critical for further learning ability that they develop their ability to distinguish between quality and quantity. However, the reason that mental workload is difficult to quantify through physiological measures, makes it more complicated to demonstrate the mental workload. When it comes to children’s development, physical change is visible and easy to identify but mental change is not. HRV is relatively easy to measure but has limitation because it is indirect way of measuring brain signal. Above all things, many researches of real-time indicator measuring physiological data such as heart rate variability (HRV) have been done sporadically but not integrated. Therefore, In this study we tried to demonstrate if we can predict the mental capacity not mental workload with the EEG. Attention ability was measured with Stroop task, and memory ability was measured with digit span task. The main outcome of this study is that building predictive models for cognitive functions using physiological measures is feasible and that its predictive models for cognitive functions using physiological measures is feasible and that its predictive power is further improved when EEG is used along with HRV data. It is implied form the outcome of study that combining physiological measures may improve its predictive power by improving the signal relative to noises and that future studies may focus on discovery of further biomarkers for prediction of cognitive functions.

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