Development of a Method for Identifying the Functional State of a

Person When Solving Cognitive Tasks According to the Data of the

Brain Microwave Radiation

Anton Yu. Dolganov

, Mikhail V. Babich

, Timur S. Petrenko

and Vladimir S. Kublanov

Ural Federal University, Yekaterinburg, Russian Federation

Keywords: Microwave Radiation, Radio Brightness Temperature, Machine Learning, Cognitive Load.

Abstract: The article described a technique for conducting experimental studies using passive non-invasive

thermography in radio bands to study the effect of changes in the psychophysiological state on a person's

radio-brightness temperature. The registration of the radio-brightness temperature signals was carried by

contact microwave radiometer, which ensures the reception of brain microwave radiation in the frequency

range (3.4-4.2) GHz. A data processing algorithm for detecting stable characteristics of patterns in changes in

brightness temperature fluctuations, based on continuous wavelet transform, was proposed. A total of 18

parameters were used to estimate the brightness temperature fluctuations. In this study, two machine learning

methods were tested that allow the selection of the most significant features: logistic regression using L1-

regularization and the decision tree method. The accuracy of determining the functional state on the training

data reached 90%. It was shown that the parameter of microwave radiation fluctuations – the median value of

the amplitude of fluctuations with fluctuation periods from 40 to 80 s, makes it possible to divide the initial

data sample into two subgroups in which the response to cognitive load is significantly different.

1 INTRODUCTION

At each moment of time, a person is in a specific

psychophysiological state. This can be a state of sleep

or wakefulness, sensory deprivation or information

overload, tension or monotony, adaptation or stress.

Each of these psychophysiological states is

characterized by a specific form of the background

activity of the nerve centers, and, accordingly, a

special systemic response of the body to external

stimuli in the form of changes in basic physiological

vital parameters such as heart rate, respiratory rate,

temperature and blood pressure. The time for the

formation of these responses and, accordingly,

changes in the psychophysiological state can be from

several seconds to tens of minutes.

The urgency of the problem of analyzing and

assessing the psychophysiological state of a person,

as a factor determining his behavior and capabilities,

https://orcid.org/0000-0003-2318-9144

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is determined by the rapid spread of cognitive

disorders among the working-age population of

developed countries (Tol et al., 2014). Existing

methods for assessing the psychophysiological state

are formed on the basis of clinical observations and

are, in fact, subjective, since they use rather

subjective questionnaires for patients, for example,

the depression anxiety stress scale (DASS), as well as

clinical interviews, for example, the Hamilton

psychometric scale (HDRS-21) for assessing the level

of depression. Known instrumental methods in this

task analyze changes in the physical characteristics of

video images or biomedical signals formed on human

skin (Ahn et al., 2019). To study deep processes in

human organs and tissues, measurements of own

physical fields are used, including microwave

radiometers and passive acoustic thermometers.

The aim of this study is to develop a method for

identifying the functional state of a person in solving

Dolganov, A., Babich, M., Petrenko, T. and Kublanov, V.

Development of a Method for Identifying the Functional State of a Person When Solving Cognitive Tasks According to the Data of the Brain Microwave Radiation.

DOI: 10.5220/0010380502330238

In Proceedings of the 14th International Joint Conference on Biomedical Engineer ing Systems and Technologies (BIOSTEC 2021) - Volume 1: BIODEVICES, pages 233-238

ISBN: 978-989-758-490-9

233

cognitive tasks based on the data of the brain's own

electromagnetic radiation.

2 MATERIALS AND METHODS

2.1 Research Methodology

Original researches on registration of radio brightness

temperature (RBT) on the basis of the Ural Federal

University were carried out. The study involved a

group of 40 relatively healthy subjects.

The cyclogram of registration of RBT consisted

of the following cycles:

 5 minutes – rest state;

 5 minutes - cognitive load (verbal counting);

 5 minutes - aftereffect.

Registration of RBT signals was carried out using

a model of a contact microwave radiometer, which

ensures the reception of the brain microwave

radiation in the frequency range (3.4-4.2) GHz

(Vesnin et al., 2019). The working frequency range

was chosen in order to be able to receive information

about RBT in the piazza and arachnoid meninges (V.

S. Kublanov et al., 2020). During the research, the

antenna unit of the radiometer was connected to the

radiometric receiver using a flexible coaxial cable 20

cm long and was located in the center of the forehead.

The radiometric receiver of the radiometer, which

provides reception of the brain microwave radiation,

had good thermal insulation from the subject's head.

To provide protection against interference, the

antenna unit was shielded with a metallized fabric.

The experiments were carried out with the lights off.

There were no mobile phones in the experiment room.

2.2 Data Processing Methods

A python program was written to process signals.

Continuous wavelet analysis was chosen as the main

processing method. (Mallat, 2009). The main libraries

used were the NumPy library for general math

transforms, the PyWT library for numerically

computing the continuous wavelet transform (Lee et

al., 2019), and scikit-learn library for applying

machine learning (ML) models(Pedregosa et al.,

2011).

The Gaussian wavelet of the eighth order was

used as the basic wavelet. For each signal, the spectral

components of the RBT signals with fluctuation

periods from 20 to 40 s, from 40 to 80 s, and from 80

to 140 s were calculated.

To evaluate the obtained spectral components of

the signals, the following approach was used,

separately for each study cycle and for each spectral

component:

 Finding all zero crossing points (green points

on Figure 1);

 Estimation for each consecutive pair of points

half-period w and amplitude h;

 Estimation of the median value M, standard

deviation STD and coefficient of variation CV

for the set of values of w and h in the study

cycle;

Figure 1: To the method for evaluating signal components.

Thus, for each time stage and for each frequency

domain of interest, 6 estimates were obtained: M w,

STD w, CV w, M h, STD h, CV h. A total of 18

parameters were used to estimate RBT fluctuations.

2.3 Visualization of the

Multidimensional Parameter Space

A popular method for studying the multidimensional

data is a method of t-SNE (Maaten & Hinton, 2008).

This method has become widespread in the field of

machine learning, because it allows one to generate

seemingly convincing two-dimensional "maps" from

data with hundreds or even thousands of parameters.

The idea behind this method is to take a set of

points in a high-dimensional space and find an exact

representation of those points in a lower-dimensional

space, usually in a 2D plane. The algorithm is non-

linear and adapts to the underlying data by

performing different transformations in different

regions.

A special feature of t-SNE is the configurable

“perplexity” parameter. In fact, this parameter

reflects how the new lower-dimensional

representation balances the local and global features

of the original data. The parameter is, in a sense, an

assumption about the number of nearest neighbors of

each point. Thus, the perplexity value has a complex

effect on the resulting images. For small values of this

parameter (1-10), the t-SNE method gives preference

to the local features of the initial data, at large values

(30-50) - the global features of the initial data.

NDNSNT 2021 - Special Session on Non-invasive Diagnosis and Neuro-stimulation in Neurorehabilitation Tasks

234

It is worth remembering the stochastic nature of

this method: the t-SNE algorithm does not always

give the same result with successive runs. Therefore,

it is recommended to repeat the run of the t-SNE

algorithm several times at certain perplexity values in

order to judge general patterns.

2.4 Machine Learning Methods

To solve the problem, an approach based on machine

learning was used, which made it possible to form

combinations of significant parameters of radio

brightness temperature fluctuations to predict the

functional state of subjects from the control group.

Machine learning is considered in a simple concept

y = f (x) , (1)

where x is the original data; f is the function obtained

using ML methods; y is the expected response. In this

study, two ML methods were chosen: logistic

regression (LogR) using L1-regularization and

decision trees (DT), which allow the selection of the

most significant features (V. Kublanov & Dolganov,

2019).

The LogR method (Meier et al., 2008) forms a

probability model using a logistic function. L1

regularization or Lasso regularization is a linear

model that estimates sparse coefficients. This

approach is useful in some problems because of its

tendency to give preference to solutions with fewer

non-zero coefficients, effectively reducing the

number of parameters on which a given solution

depends. Mathematically, this model is based on

adding a regularization term to the standard LogR

approach - the modulus of the sum of the weight

coefficients.

The DT method is also an approach that

incorporates the search for the most significant

parameters (Lior, 2014). At each DT node, there is a

search among all available parameters for the one that

most optimally divides the sample into classes. The

selection takes place sequentially until all data is

divided or further separation is less optimal than what

was obtained in the previous step.

The cycle of studies is considered as y. The

analyzed signal is expected to be related to brain

activity and should look different for different study

cycles. As the initial data x, 18 parameters of RBT

fluctuations were considered.

The following were used as the metrics for

evaluating the models of ML:

 accuracy - the proportion of responses

correctly predicted by the ML model;

 precision - the proportion of responses

predicted by the ML model are positive and, at

the same time, are really positive;

 recall - the proportion of objects predicted by

the ML model as a positive class out of all

objects of a positive class;

 F1-score - harmonic mean of precision and

recall.

To reduce the effect of overfitting, a 5-fold cross-

validation approach was used (Refaeilzadeh et al.,

2009). At each stage of the cross validation, each

metric was evaluated. The final metrics were assessed

as the average of the metrics across 5 rounds of cross

validation (cv5). Additionally, the overall

classification accuracy and the F-1 measure were

evaluated for the general model (on the training

data).

3 RESULTS

3.1 Data Visualization with t-SNE

Figure 2 shows the results of visualizing the

application of the t-SNE algorithm for three

perplexity values- 2, 10, 30. For each perplexity

value, the t-SNE algorithm was implemented several

times. Below are the 4 most typical implementations

for each perplexity value.

As can be seen from Figure 2, at different

perplexity values, green dots, which correspond to

cognitive load, quite often turn out to be isolated from

red and blue. In this case, the red and blue dots, as a

rule, are close to each other. In some cases, green dots

are divided into subgroups, due to the nature of the t-

SNE algorithm. However, even in this cases these

groups are separated from the red and blue dots.

Thus, the parameters of RBT fluctuations at the

stages of rest state and aftereffect are quite similar,

while the parameters at the stage of cognitive load

tend to differ from both of these stages. From this, we

can conclude that it would be more reasonable to

solve the binary classification problem: the rest state

recording and aftereffect stage was selected as the

first class, and the cognitive load stage as the second

class.

3.2 Results of Applying Machine

Learning Methods

Table 1 shows the results of evaluating the MO

models. For LogR, two models are considered - linear

combinations of parameters and combinations of

second degree polynomials. Increasing the degree of

the polynomial to 3 does not increase the performance

Development of a Method for Identifying the Functional State of a Person When Solving Cognitive Tasks According to the Data of the Brain

Microwave Radiation

235

Figure 2: Visualization of RBT parameters by the t-SNE method: a) perplexity = 2; b) perplexity = 10; c) perplexity = 30.

Red dots – rest state recording, green - cognitive load, blue - aftereffect.

metrics of the MO model. For DT, the parameter

maximum depth is set equal to 5. At this value of the

parameter, the highest values of the MO metrics are

achieved.

Table 1: Perfomance metrics for classification models.

Metric

LogR

(linear)

LogR

(Quadratic)

Accuracy_cv5

0.650

0.625 0.591

Precision_cv5

0.798

0.727 0.705

Recall_cv5 0.662

0.725

0.700

F1-score_cv5

0.719

0.716 0.693

Accuracy_Train 0.675 0.800

0.900

F1-score_Train 0.738 0.850

0.923

Number of

parameters

13 10

As can be seen from the results shown in Table 1,

the highest accuracy for training data is achieved

using the DT method. Moreover, for this method, the

MO metrics on cross validation are lower than for

LogR.

Analyzing the list of parameters for different ML

models, it is worth highlighting the parameter

M H 40-80 (median value of the

RBT

amplitude with

a fluctuation period from 40 to 80 s). This parameter

was present in all ML models. If one analyzes this

parameter separately, it can be seen that the

distribution of this parameter for the functional state

of the cognitive load looks biased in relation to the

states of rest state and aftereffect.

Let's divide the initial sample into two: parameter

M H 40-80 in the state of cognitive load ≥678 (the

first group); parameter M H 40-80 in a state of

cognitive load <678 (the second group).

Visualization of the distribution of the parameter

M H 40-80 for different functional states for these

two groups is shown in Figure 3. It can be seen that

the first group is characterized by an increase in this

NDNSNT 2021 - Special Session on Non-invasive Diagnosis and Neuro-stimulation in Neurorehabilitation Tasks

236

Figure 3: Distribution of the parameter M H 40-80:

a) the first group; b) the second group. In light highlighted

the distribution for the rest state stage, in pink - for the

cognitive load stage, in dark - for the after-effect stage.

parameter in a state of cognitive load. For the second

group, the distributions of this parameter are similar.

Estimates of the Student's t-test for the first group

showed that the difference in means is statistically

significant:

 in comparison with the rest-state stage, record

t-test = 3.87, p-value = 0.0002;

 in comparison with the after-effect stage t-test

= 2.62, p-value = 0.0103.

For the second group, the Student's t-test did not

show the statistical significance of the difference in

means. This result may indicate that the original

sample can be divided into two subgroups in which

the response to cognitive load is different. These two

subgroups require a more detailed comparison, taking

into account the psychophysiological and

motivational characteristics of the subjects.

4 CONCLUSIONS

The article describes a technique for conducting

experimental studies using passive non-invasive

thermography methods in radio bands to study the

effect of changes in the psychophysiological state on

a person's radio brightness temperature. The study

consists of three stages - background recording,

cognitive load (verbal counting) and aftereffect. To

model of a contact microwave radiometer was used,

which ensures the reception of brain microwave in the

frequency range (3.4-4.2) GHz.

Correction of data processing algorithms is

proposed to detect stable characteristics of patterns in

changes in radio brightness temperature fluctuations,

which are determined by changes in the

psychophysiological state of a person. For each

signal, a wavelet analysis was performed to calculate

the spectral components of the brightness temperature

signals with fluctuation periods from 20 to 40 s, from

40 to 80 s, and from 80 to 140 s. For each stage of the

study, 18 parameters were assessed: mean value M,

standard deviation STD and coefficient of variation

CV for the set of values of the width and amplitude of

the oscillation for each of the three spectral

components.

An assessment of the performance of algorithms

based on machine learning methods for the formation

of an objective assessment of the

psychophysiological state of a person by recording

the brain microwave radiation, has been carried out.

The results presented made it possible to single

out a special parameter of brightness temperature

fluctuations - the average value of the amplitude of

fluctuations with fluctuation periods from 40 to 80 s.

This parameter makes it possible to divide the initial

data sample into two subgroups in which the response

to cognitive load is significantly different.

ACKNOWLEDGEMENTS

The reported study was funded by RFBR according

to the research project № 18-29-02052.

)

Development of a Method for Identifying the Functional State of a Person When Solving Cognitive Tasks According to the Data of the Brain

Microwave Radiation

237

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