@article{(Open Science Index):https://publications.waset.org/pdf/10002036,
	  title     = {EEG Correlates of Trait and Mathematical Anxiety during Lexical and Numerical Error-Recognition Tasks},
	  author    = {Alexander N. Savostyanov and  Tatiana A. Dolgorukova and  Elena A. Esipenko and  Mikhail S. Zaleshin and  Margherita Malanchini and  Anna V. Budakova and  Alexander E. Saprygin and  Tatiana A. Golovko and  Yulia V. Kovas},
	  country	= {},
	  institution	= {},
	  abstract     = {EEG correlates of mathematical and trait anxiety level
were studied in 52 healthy Russian-speakers during execution of
error-recognition tasks with lexical, arithmetic and algebraic
conditions. Event-related spectral perturbations were used as a
measure of brain activity. The ERSP plots revealed alpha/beta
desynchronizations within a 500-3000 ms interval after task onset
and slow-wave synchronization within an interval of 150-350 ms.
Amplitudes of these intervals reflected the accuracy of error
recognition, and were differently associated with the three conditions.
The correlates of anxiety were found in theta (4-8 Hz) and beta2 (16-
20 Hz) frequency bands. In theta band the effects of mathematical
anxiety were stronger expressed in lexical, than in arithmetic and
algebraic condition. The mathematical anxiety effects in theta band
were associated with differences between anterior and posterior
cortical areas, whereas the effects of trait anxiety were associated
with inter-hemispherical differences. In beta1 and beta2 bands effects
of trait and mathematical anxiety were directed oppositely. The trait
anxiety was associated with increase of amplitude of
desynchronization, whereas the mathematical anxiety was associated
with decrease of this amplitude. The effect of mathematical anxiety
in beta2 band was insignificant for lexical condition but was the
strongest in algebraic condition. EEG correlates of anxiety in theta
band could be interpreted as indexes of task emotionality, whereas
the reaction in beta2 band is related to tension of intellectual
resources.},
	    journal   = {International Journal of Medical and Health Sciences},
	  volume    = {9},
	  number    = {7},
	  year      = {2015},
	  pages     = {554 - 558},
	  ee        = {https://publications.waset.org/pdf/10002036},
	  url   	= {https://publications.waset.org/vol/103},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 103, 2015},
	}