http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Predicting Attention and Memory Ability based on the Combination of EEG and HRV data in Children
전형석(Hyeong Seok Jeon),이준희(Junhee Lee),이준(June Lee),황유진(You Jin Hwang),이시영(Siyoung Lee),양희(Hee Yang),윤정한(Jung Han Yoon),권준수(Jun Soo Kwon),원중호(Jung Ho Won),조준동(Jun Dong Cho),이기원(Ki Won Lee) 한국교원대학교 뇌기반교육연구소 2019 Brain, Digital, & Learning Vol.9 No.3
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.