An Automatic EEG Based System for the Recognition of Math Anxiety

M. Klados, N. Pandria, A. Athanasiou, P. Bamidis
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引用次数: 9

Abstract

Mathematical Anxiety is the feeling of fear or dislike when dealing with mathematical rich situations. Although math anxiety seems to be innocent it can seriously affect so the learning procedure, as the future carrier directions. The accurate recognition of math anxiety is very important so for diagnostic purposes as for e-learning systems. This work comes to present an automatic system for the detection of math anxiety based on electroencephalographic (EEG) signals, that are supposed to be more subjective, compared to self-report and psychometric questionnaires, since they cannot be intentionally modulated. For this reason we have gathered multichannel EEG recordings from two groups with different levels of math anxiety (Low and High). From these EEG signals we have extracted 466 features and then using a feature selection algorithm we ended to only one feature that was able to recognize math anxiety with 93.75% accuracy using a Naïve Bayesian Tree with 10-fold cross validation
基于脑电图的数学焦虑自动识别系统
数学焦虑是在处理数学丰富的情况时感到恐惧或不喜欢的感觉。虽然数学焦虑看起来是无辜的,但它可以严重影响学习过程,作为未来的载体方向。准确识别数学焦虑对于诊断和电子学习系统都是非常重要的。这项工作提出了一个基于脑电图(EEG)信号检测数学焦虑的自动系统,与自我报告和心理测量问卷相比,脑电图信号应该更加主观,因为它们不能被有意地调节。出于这个原因,我们收集了两组不同数学焦虑水平(低和高)的多通道脑电图记录。从这些脑电图信号中,我们提取了466个特征,然后使用特征选择算法,使用具有10倍交叉验证的na&# xef; 5贝叶斯树,我们最终只有一个特征能够以93.75%的准确率识别数学焦虑
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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