Volume 33 #2

Contents

1. Computation of eye optics of the cetacean’s eye
2. Neurophysiological mechanisms of two sequential orientations comparison in working memory
3. Contribution of the marginal peripheral retina to color constancy: Evidence obtained due to contact lens with implanted occluder
4. Contribution of the spectral and temporal mechanisms to analysis of complex sound signals
5. Role of the donor of NO molecules in regulation of primary sensory neuron responses
6. Identity documents forgery detection with mobile devices
7. Transient processes of visual evoked potentials in the tasks of human-computer interfaces
8. Algebraic reconstruction in case of limited GPU memory in the task of computed tomography
9. New criteria for neural network encoder learning in the string segmentation problem

The paper analyzes the transients between states of steady-state visual evoked potentials (SSVEP) under conditions of varying the frequency of photostimulation (FS), which simulates the operation of a brain-computer synchronous interface. The dependences of SSVEP parameters on FS frequencies are investigated. The cluster analysis method (K-means) revealed three data clusters differing in the features of the response to the FS and the distribution of the EEG channels that demonstrate it. It has been established that in some cases the percentage of people “generating” a signal of the required frequency after changing the mode of photostimulation was significantly lower than 100%, up to the cases of complete absence of “signal detection” by any of the subjects. The latent time of occurrence of pronounced frequency components for the studied signals ranged from 150 to 420 ms, which makes it possible to consider this time as a prospect for indicators of the brain-computer interfaces speed. When using the chains of local maxima of the square matrix of the wavelet transform instead of wavelet filtering, the high-frequency component was identified better than the low-frequency one. When using wavelet filtering, an inverse relationship was revealed.

Key words: SSVEP, brain-computer synchronous interfaces, wavelet transformation

DOI: 10.1134/S0235009219020100

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