The study of cognitive processes in the brain EEG during the perception of bistable images using wavelet skeleton

In the paper we study the appearance of the complex patterns in human EEG data during a psychophysiological experiment by stimulating cognitive activity with the perception of ambiguous object. A new method based on the calculation of the maximum energy component for the continuous wavelet transform (skeletons) is proposed. Skeleton analysis allows us to identify specific patterns in the EEG data set, appearing in the perception of ambiguous objects. Thus, it becomes possible to diagnose some cognitive processes associated with the concentration of attention and recognition of complex visual objects. The article presents the processing results of experimental data for 6 male volunteers.

[1]  Alexey A. Koronovskii,et al.  Method for diagnostics of characteristic patterns of observable time series and its real-time experimental implementation for neurophysiological signals , 2011 .

[2]  Leslie G. Ungerleider,et al.  The neural systems that mediate human perceptual decision making , 2008, Nature Reviews Neuroscience.

[3]  Alexey A Koronovskii,et al.  Detecting synchronization of self-sustained oscillators by external driving with varying frequency. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  I. Merk,et al.  A stochastic model of multistable visual perception , 2002, Biological Cybernetics.

[5]  Paul M Bays,et al.  Working memory retrieval as a decision process. , 2014, Journal of vision.

[6]  Alexander Hramov,et al.  On-off intermittency in time series of spontaneous paroxysmal activity in rats with genetic absence epilepsy. , 2006, Chaos.

[7]  Alexander E. Hramov,et al.  On–off intermittency of thalamo-cortical oscillations in the electroencephalogram of rats with genetic predisposition to absence epilepsy , 2012, Brain Research.

[8]  Alexander E. Hramov,et al.  Time-frequency characteristics and dynamics of sleep spindles in WAG/Rij rats with absence epilepsy , 2014, Brain Research.

[9]  Alexander E. Hramov,et al.  Sleep spindles and spike–wave discharges in EEG: Their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis , 2009, Journal of Neuroscience Methods.

[10]  Alexey Ovchinnikov,et al.  An algorithm for real-time detection of spike-wave discharges in rodents , 2010, Journal of Neuroscience Methods.

[11]  Jochen Braun,et al.  Bistable Perception Modeled as Competing Stochastic Integrations at Two Levels , 2009, PLoS Comput. Biol..

[12]  John Rinzel,et al.  Noise and adaptation in multistable perception: noise drives when to switch, adaptation determines percept choice. , 2014, Journal of vision.

[13]  Alexey N. Pavlov,et al.  Wavelet analysis in neurodynamics , 2012 .

[14]  R. Blake,et al.  Neural bases of binocular rivalry , 2006, Trends in Cognitive Sciences.

[15]  Philip L. Smith,et al.  A comparison of sequential sampling models for two-choice reaction time. , 2004, Psychological review.

[16]  M. O. Zhuravlev,et al.  Experimental measurements of human brain noise intensity in perception of ambiguous images , 2016 .

[17]  Alexey N. Pavlov,et al.  Wavelets in Neuroscience , 2014, Springer Series in Synergetics.

[18]  Alexey A Koronovskii,et al.  Detection of synchronization from univariate data using wavelet transform. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  World Medical Association (WMA),et al.  Declaration of Helsinki. Ethical Principles for Medical Research Involving Human Subjects , 2009, Journal of the Indian Medical Association.

[20]  G. Rees,et al.  The Neural Bases of Multistable Perception , 2022 .

[21]  Alexey N. Pavlov,et al.  Time-frequency dynamics during sleep spindles on the EEG in rodents with a genetic predisposition to absence epilepsy (WAG/Rij rats) , 2015, Saratov Fall Meeting.

[22]  Alexander N. Pisarchik,et al.  Critical slowing down and noise-induced intermittency in bistable perception: bifurcation analysis , 2014, Biological Cybernetics.

[23]  Alexey N. Pavlov,et al.  Analysis of the characteristics of the synchronous clusters in the adaptive Kuramoto network and neural network of the epileptic brain , 2016, Saratov Fall Meeting.

[24]  N. N. Available.  World medical association declaration of Helsinki , 2000, Chinese Journal of Integrative Medicine.

[25]  J. Rinzel,et al.  Noise-induced alternations in an attractor network model of perceptual bistability. , 2007, Journal of neurophysiology.

[26]  Alexander Hramov,et al.  Spike–wave discharges in WAG/Rij rats are preceded by delta and theta precursor activity in cortex and thalamus , 2011, Clinical Neurophysiology.

[27]  Alexander N. Pisarchik,et al.  Controlling bistability in a stochastic perception model , 2015 .

[28]  N. Logothetis,et al.  Multistable phenomena: changing views in perception , 1999, Trends in Cognitive Sciences.

[29]  A. Selverston,et al.  Dynamical principles in neuroscience , 2006 .

[30]  Julien C Sprott,et al.  The Role of Depth and 1/f Dynamics in Perceiving Reversible Figures , 2003, Nonlinear dynamics, psychology, and life sciences.

[31]  Vladimir A. Maksimenko,et al.  Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models , 2016, Journal of Neuroscience Methods.

[32]  Xiao-Jing Wang.  Neural dynamics and circuit mechanisms of decision-making , 2012, Current Opinion in Neurobiology.

[33]  Alexey A Koronovskii,et al.  An approach to chaotic synchronization. , 2004, Chaos.

[34]  Alexey A. Koronovskii,et al.  Method of studying the synchronization of self-sustained oscillations using continuous wavelet analysis of univariant data , 2007 .