Predicting Attention and Memory Ability based on the Combination of EEG and HRV data in Children
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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.
Abstract
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.
참고문헌 (28)
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