TY - GEN
T1 - An Automatic EEG Based System for the Recognition of Math Anxiety
AU - Klados, Manousos A.
AU - Pandria, Niki
AU - Athanasiou, Alkinoos
AU - Bamidis, Panagiotis D.
PY - 2017/11/13
Y1 - 2017/11/13
N2 - 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.
AB - 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.
KW - Automatic Recognition of Anxiety
KW - EEG
KW - Math Anxiety
KW - Mathematical Cognition
UR - http://www.scopus.com/inward/record.url?scp=85040369672&partnerID=8YFLogxK
U2 - 10.1109/CBMS.2017.107
DO - 10.1109/CBMS.2017.107
M3 - Conference publication
AN - SCOPUS:85040369672
VL - 2017-June
T3 - Proceedings IEEE International Symposium on Computer-Based Medical Systems
SP - 409
EP - 412
BT - Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017
PB - IEEE
T2 - 30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017
Y2 - 22 June 2017 through 24 June 2017
ER -