English ability score prediction algorithm based on prefrontal cortex blood volume utilizing a regulated linear regression model

Kosho Oki, Y. Kurihara, T. Kaburagi, K. Shiba
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Abstract

English is becoming a common language in our global society. Moreover, the verification of English ability is important. The Test of English for International Communication (TOEIC) is representative of a method to estimate English ability quantitatively. However, a significant amount of time is required to take TOEIC. For this reason, an easier measure of English ability is desirable. In this paper, we propose a method to predict English ability from changes in cerebral oxy- and deoxy-hemoglobin (Hb) concentrations by using 10-channel prefrontal cortex near-infrared spectroscopy data at a resting state. The data is obtained when the subjects are solving an English problem. Our proposed system could estimate 11 subjects' TOEIC scores with a 9.06% error rate.
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基于调节线性回归模型的前额皮质血容量英语能力评分预测算法
英语正在成为我们全球社会的通用语言。此外,英语能力的验证也很重要。国际交流英语考试(TOEIC)是一种定量评估英语能力的典型方法。但是,参加托业考试需要大量的时间。出于这个原因,我们需要一个更简单的英语能力衡量标准。在本文中,我们提出了一种利用静息状态下10通道前额皮质近红外光谱数据,从大脑氧和脱氧血红蛋白(Hb)浓度的变化来预测英语能力的方法。这些数据是在受试者解决英语问题时获得的。我们提出的系统可以估计11个科目的托业成绩,错误率为9.06%。
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