Victor A. Sadovnichy

Sadovnichy, V.A.

Acad.

Doctor of Science

Lomonosov Moscow State University, Moscow, Russia

Rector of Lomonosov Moscow State University
Moscow

Publications About the author

Automated real-time classification of functional states: the significance of individual tuning stage

Galatenko, V.V.; Livshitz, E.D.; Chernorizov, Alexsander M.; Zinchenko, Yu. P. ; Galatenko, A.V.; Staroverov, V.M.; Isaychev, S.A.; Lebedev, V.V.; Menshikova, G.Ya.; Sadovnichy, V.A.; Gusev, A.N.; Gabidullina, R.F.; Podol’skii, V.E.

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Volume 6, Issue 3, 2013

Vladimir V. Galatenko, Evgeniy D. Livshitz, Alexander M. Chernorizov, Yury P. Zinchenko, Alexey V. Galatenko, Vladimir M. Staroverov, Sergey A. Isaychev, Vyacheslav V. Lebedev, Galina Ya. Menshikova, Alexey N. Gusev, Ekaterina M. Lobacheva, Rozaliya F. Gabidullina, Vladimir E. Podol’skii, Victor A. Sadovnichy (2013). Automated real-time classification of functional states: the significance of individual tuning stage. Psychology in Russia: State of the Art, 6(3), 40-47

“ CITE
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- Automated classification of a human functional state is an important problem, with applications including stress resistance evaluation, supervision over operators of critical infrastructure, teaching and phobia therapy. Such classification is particularly efficient in systems for teaching and phobia therapy that include a virtual reality module, and provide the capability for dynamic adjustment of task complexity.
  In this paper, a method for automated real-time binary classification of human functional states (calm wakefulness vs. stress) based on discrete wavelet transform of EEG data is considered. It is shown that an individual tuning stage of the classification algorithm — a stage that allows the involvement of certain information on individual peculiarities in the classification, using very short individual learning samples, significantly increases classification reliability. The experimental study that proved this assertion was based on a specialized scenario in which individuals solved the task of detecting objects with given properties in a dynamic set of flying objects.
  
  DOI: 10.11621/pir.2013.0304
  
  Keywords: human functional state, EEG data, automated classification, individual tuning, stress.

Full member of the Russian Academy of Science Mathematics Department, Applied Mathematics and Computer Science Section (1997, corresponding member since 1994). Bureau member of the Russian Academy of Science Mathematics Department (1996). Member of the Russian Academy of Science Presidium (1996).