Abstract
The paper verifies the hypothesis of time-dependent dynamics of steady-state visual evoked potentials during a short series of stimulations (15 s) simulating work with brain-computer interfaces. Using deep machine learning of a neural network with direct propagation and known methods of machine classification, the frequency characteristics of the visual evoked potentials of electroencephalography during the work with brain-computer interfaces are analyzed. It is shown that the temporal dynamics of steady-state visual evoked potentials even for such a short period of time can sufficiently change the parameters. It can potentially serve as an obstacle for work with this type of brain-computer interfaces for a number of users. The described approach allows us to confirm the hypothesis that over time the brain shows signs of fatigue, consisting in changes in the frequency-time characteristics of the registered signal. Thus, the human brain shows signs of fatigue during sessions of steady-state visual evoked potentials.
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This work was supported by RFBR grant 19–29-01156 mk.
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Turovsky, Y., Wolf, D., Meshcheryakov, R., Iskhakova, A. (2022). Dynamics of Frequency Characteristics of Visually Evoked Potentials of Electroencephalography During the Work with Brain-Computer Interfaces. In: Prasanna, S.R.M., Karpov, A., Samudravijaya, K., Agrawal, S.S. (eds) Speech and Computer. SPECOM 2022. Lecture Notes in Computer Science(), vol 13721. Springer, Cham. https://doi.org/10.1007/978-3-031-20980-2_57
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