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Section BRAIN-COMPUTER INTERFACES, COGNITIVE NAVIGATION WORKSHOP AND NEUROENGINEERING
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5. Ravaja N., Somervuori O., Salminen M. Predicting purchase decision: The role of hemispheric asymmetry over the frontal cortex //Journal of Neuroscience, Psychology, and Economics. - 2013. - T. 6. - №. 1. - C. 1.
OM&P
Steady State Visual Evoked Potential Based BCI Study in Overt and Covert Attention
Zafer Iscan1 *, Elena Sokolova2'3, Vadim V. Nikulin1,4
1 Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russian Federation;
2 Faculty of Materials Science, Moscow State University, Moscow, Russian Federation ;
3 Energy Science and Technology, Skolkovo Institute of Science and Technology, Moscow, Russian Federation;
4 Neurophysics Group, Department of Neurology, Charité - University Medicine Berlin, Berlin, Germany. * Presenting e-mail: zaferiscan@yahoo.com
Introduction
Brain-computer interfaces (BCIs) have potential to help severely disabled people by translation of the intentions of subjects into a number of different commands. Due to its safety and high time resolution, electroencephalogram (EEG) based BCIs have become popular and various designs using different signals (e.g. P300, oscillations) have been proposed. Among them, steady state visual evoked potentials (SSVEPs) are particularly attractive due to their high signal to noise ratio (SNR). In this study, we proposed a four-class BCI design based on SSVEPs to study the differences between the overt and covert attention.
Methods
Four circles with individual flickering frequencies (5.45, 8.57, 12 and 15 Hz) were presented to healthy participants on an LCD monitor. EEG was recorded from 60 channels with three electrooculogram (EOG) channels in 30 trials. In each trial, subjects focused either on the fixation cross or one of the four circles and paid attention to the circle indicated by a red oval frame for three seconds. Decision tree, Naive Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis.
Results
The offline classification accuracy for the overt attention was positively correlated with the duration of stimuli and was more than 90% when it was longer than two seconds. The accuracy dropped drastically in the covert attention case. Discussion: Classification performance for the overt attention condition validates the robustness of the SSVEP-based BCIs. However, different classification approaches should be developed in order to classify the covert attention responses.
Experimental Measurements of Human Brain Noise Intensity in Perception of Ambiguous Images
Alexander E. Hramov1,2, Vadim V. Grubov1,2, Alexey A. Koronovskii1,2 *, Maria K. Kurovskaya1,2, Anastasiya E. Runnova1,2,Maxim O. Zhuravlev1,2, Alexander N. Pisarchik3,4
1 Saratov State University, Astrakhanskaya, 83, Saratov, 410012, Russia
2 Saratov State Technical University, Politehnicheskaya, 77, Saratov, 410054, Russia;
3 Centro de Investigaciones en Optica, Loma del Bosque 115, Lomas del Campestre, 37150 Leon, Guanajuato, Mexico;
4 Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, 28223 Pozuelo de Alarcon, Madrid, Spain.
* Presenting e-mail: alexey.koronovskii@gmail.com
66 Opera Med Physiol 2016 Vol. 2 (S1)
