analysis of brain activity period in various...

Viktor Minkin, Andrey Kachalin20

DOI: 10.25696/ELSYS.VC2.EN.16

ANALYSIS OF BRAIN ACTIVITY PERIOD IN VARIOUS HUMAN ACTIVITIES BY VIBRAIMAGE

TECHNOLOGYViktor Minkin, Andrey Kachalin

elsys Corp., Russia, st. Petersburg, [email protected]

Abstract: An experimental study of a person’s brain activity period in the very low frequen-cy (VLF) range during the performance of three different activities. The variation in brain activ-ity period according to type of work being performed is demonstrated. A hypothesis is proposed explaining the dependence of brain activity period on the brain’s mental load.

Keywords: vibraimage, psychophysiology, brain activity period (BAP), natural regulation of brain activity, response to stimuli, VLF.

In the study of the psychophysiological mechanisms of brain activity period formation (Minkin, Blank, 2019), it was hypothesized that the period of brain activity in the VLF range (very low frequency, period of 30–60 seconds) (Fleishman, 1999, 2014, 2015) is a function of the mental burden on the brain. The purpose of the present study was to test this hypothesis and to obtain statistically reliable measurements of the brain activity period of a testee solving various mental problems.

Methods and MaterialsWe measured the brain activity period of a subject, a 29-year-old man who

works as a programmer and who holds the title of CM in chess. The measurements were obtained during working hours while the subject performed production tasks from 11 a.m. to 6 p.m. in March 2019. The subject’s activities consisted of programming and working with documentation. As a break between solving production problems, the subject played blitz games of chess online with a limit of one minute per game (approx. 1 move every 2 seconds). The period of brain activity was measured by vibraimage technology (Minkin, 2008, 2017, 2018) while controlling the vestibular-emotional reflex (Minkin & Nikolaenko, 2008, Blank et al., 2012).

Microsoft LifeCam Studio web camera with a resolution of 640×480 elements and frame rate of 30 Hz was fixed to the monitor in front of the test subject. The image of the subject’s head on the webcam’s photodetector was not less than 200 elements horizontally. Video processing and the determination of data on the current value of the subject’s psychophysiological state (PPS) was determined by VibraMed10 program (VibraMed10, 2019). The default program settings were used, except for the PPS measurement time, which was set to 380 s.

The statistical processing of the results was done by the VibraStat program (VibraStat, 2019), which carried out the summation and averaging of the current

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Viktor Minkin, Andrey Kachalin

AnALYsIs oF BRAIn ACtIVItY PeRIoD In VARIoUs HUMAn ACtIVItIes BY VIBRAIMAGe teCHnoLoGY 21

PPS using the Fourier fast transformation (FFT) algorithm (Heideman et al., 1984). 78 measurement results were captured while the subject was occupied with documentation, 38 measurement results were captured while he was occupied with programming, and 38 measurement results were captured while he was playing chess. Each PPS measurement by VibraMED 10 testing included 380×5 = 1900 counts of the PPS, since the frequency of PPS measurements was 5 counts per second.

ResultsThe results of FFT processing and the averaging of 78 PPS spectrograms when the

subject was occupied with documentation and reading technical literature are given in Figure 1.

Fig. 1. Averaged FFt spectrogram of PPs changes in subject’s brain activity when occupied with documentation

The averaged spectrogram obtained in Figure 1 has a maximum of 48.5 seconds. The maximum determined by the mean and median value when averaging the Fourier spectrograms coincides with this value.

The results of the FFT spectrogram averaging when measuring the subject’s PPS during programming are shown in Figure 2.

The spectrogram obtained in Figure 2 has a maximum of 42.5 seconds. In this case, the maximum determined by the mean and median values when averaging the Fourier spectrograms coincided with the results.

The results of averaging the FFT spectrograms measuring the subject’s PPS while playing chess are shown in Figure 3.


Fig. 2. Averaged FFt spectrogram of PPs changes for subject’s brain activity when occupied with programing

Fig. 3. Averaged FFt spectrogram of PPs changes for subject’s brain activity while playing chess

While these measurements were being taken, the subject was engaged in playing chess. The spectrogram obtained in Figure 3 has a maximum of 34 seconds, meaning the period of maximum brain activity was 34 seconds.


Figure 4 shows the dependence of the maximum value (brain activity periods) of the averaged spectrograms of the data in Figures 1–3 on the type of activity the subject was engaged in while being tested.

Fig. 4. Dependence of brain activity period (in seconds) on type of activity

The number of tests for different types of activity turned out to vary based on the subject’s current production load, since one of the objectives of this study was to influence the subject’s standard production process as little as possible. To that point, the identity of the maximum estimates for the arithmetic mean and median average confirms the reliability of the estimates obtained (Novitsky, 1975).

Discussion of the ResultsStudies have demonstrated the dependence of the period of change in PPS on the

type of activity of the subject being tested. Since the subject was not engaged in physical labor during this test, it had previously been proposed (Minkin, 2019) to associate the change in the subject’s PPS with the burden on the brain. Consequently, it is logical to assume that a difference in mental burden on the brain leads to a change in the period of brain activity, just as an increase in the physical load on the human body leads to an increase in heart rate (Fleischman, 1999).

Since the period is an inverse function of frequency, then, by analogy with the heart rate, it turns out that the subject expends the maximum energy when playing chess, slightly less when programming, and working with documents takes the smallest mental toll. It is interesting to note that, according to the subject’s testimony, he considers himself excellent at chess, average at programming, and below average


in working with documents. If we summarize the data, it turns out that the brain activity that is pleasant for a person places a greater burden on the brain and requires a large amount of energy from it, while an activity that is unpleasant for a person requires less brain activity.

To clarify, we mean the physical expenditure of energy on brain activity. Physically, the assumption looks logical enough, because if a person is highly skilled at a certain kind of activity, then naturally her brain works as efficiently as possible, that is, it performs the maximum number of operations, and the maximum number of information transfers between brain neurons occurs. Activity that is unpleasant is characterized by low efficiency and low transmission of information between the neurons of the brain, which means less energy is spent on it. Some may argue that this is nothing new, that this has all been discussed before by Wiener and Bernstein (Wiener, 1948; Bernstein, 1967), and are partially correct. But this study succeeded in obtaining experimental confirmation of the human cybernetic model using vibraimage technology to analyze the work of the vestibular system.

Analysis of the vestibular system functions turned out to be more effective psychophysiological tests than EEG, heart rate, and MRI studies (Standards, 2014), most likely due to the following reasons. The functioning of the vestibular system in the control of the head’s micro-movements automatically filters high-frequency oscillations due to mechanical inertia. These same high-frequency processes are the main subject of study when it comes to EEG and heart rate. MRI equipment doesn’t allow for normal human activity, so it is quite difficult to conduct similar studies and collect statistics in hundreds of measurements during MRI studies.

Of course, this study needs independent confirmation, which is quite simple to do since VibraMed10 program (VibraMed10, 2019) has a Demo mode that allows any interested party to conduct a similar study.

ConclusionsThe results of this study confirm the hypothesis that the brain activity period

measured by vibraimage technology is dependent on the type of activity and brain load of a person.

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