An Automatic EEG Based System for the Recognition of Math Anxiety

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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 Naive Bayesian Tree with 10-fold cross validation.

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Publication date13 Nov 2017
Publication titleProceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017
PublisherIEEE
Pages409-412
Number of pages4
Volume2017-June
ISBN (Electronic)9781538617106
Original languageEnglish
Event30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017 - Thessaloniki, Greece
Duration: 22 Jun 201724 Jun 2017

Publication series

NameProceedings IEEE International Symposium on Computer-Based Medical Systems
PublisherIEEE
ISSN (Print)2372-9198

Conference

Conference30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017
CountryGreece
CityThessaloniki
Period22/06/1724/06/17

    Keywords

  • Automatic Recognition of Anxiety, EEG, Math Anxiety, Mathematical Cognition

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