Electroencephalograms (EEG) During Mental Arithmetic Task Performance

Abstract

The aim of the present study is to collect the electrical activity of the brain using electroencephalogram (EEG) data from subjects performing a cognitive workload task. During the study, the subjects were involved in intense cognitive activity while performing mental calculations (serial subtractions). The background EEG was recorded from each subject. The primary aim for collecting this dataset is to conduct fluctuation analysis of the EEG during cognitive activity and to compare the result to the data provided by conventional methods, such as Fourier power spectral density mapping and coherence. It can also be utilized for studying the time-scale characteristics of the involvement of different brain areas in cognition processes and nonlinear characteristics of brain dynamics.

Introduction

I. INTRODUCTION

Mental disease is now a widespread and prevalent illness in the human body, and it has claimed the lives of many people throughout the world. The study of human cognitive activity has attracted a lot of interest from researchers of different domains from biophysics to system analysis and digital signal processing. The most important behavioral risk factors of mental disease and stroke are unhealthy diet, physical inactivity and harmful use of alcohol. Health policies that create conducive environments for making healthy choices affordable and available are essential for motivating people to adopt and sustain healthy behaviors. Many theoretical and practical works have been published over the last decades and activity for brain about the relationships between cognitive phenomena and activity of brain structure. The theory of brain function using structure during emotion and mental activities and dynamical properties of cortical areas and coordination are stimulated.The study of human cognitive activity has attracted a lot of interest from researchers of different domains, from biophysics from the system and system analysis and digital signal processing.

One of the main fields is the study of brain activity dynamics during emotional states that are integrated with cognitive processes. Many theoretical and practical works have been published over the last decades and activity for brain about the relationships between cognitive phenomena and activity of brain structure.

The activation and functional of brain areas during cognitive activity has been mainly studied to the special technique of the spectral. During cognitive activity this are powerful tools used to reveal the important characteristics of brain functioning.

Achieving these targets will require significant investment in and strengthening of mental systems. The study of human cognitive activity has attracted a lot of interest from researchers of different domains from biophysics to system analysis and digital signal processing.

II. LITERATURE REVIEW

In this base paper the PCA Algorithms formation the present study of the data set is to be accompanied for each and every formation of modules. Hence, we use the algorithms processes to derive this path and a set of objectives of the processes of algorithms. This survey is to be accompanied by modules. As per results, Tara Shevchenko proposed by combining the modulations of functions and hence these are restored by modules by any form of errors. These are overloaded for the modules and hence these literatures project is to be used. As a result,  these tools used to revel the important characteristics of brain functioning and reveal the important characteristics of brain functioning.

These are the basic propagations of these types of networks and thus indeed the project that are to be sustained. The application is gained much popularity in the recent years due to the accurate accuracy and efficiency of making predictions. The importance of research in this area lies in the possibility to develop and select models with the highest accuracy and efficiency.

III. DATA DESCRIPTION

A. EEG Recording

The EEGs were recorded using neuron monopolar EEG 23- Channel system. Silver chloride electrodes were placed on the scalp at symmetric anterior frontal, central, parietooccipital and temporal recording sites according to schemes.

Hence, the descriptions are referred that electrodes were interconnected ear references electrodes. The inter- electrodes are to being interconnected by a reference models. These models are to be recorded by 4 digits of numbers and hence these should be used.

B. EEG Selection

These processes are thus select through select artificial- free EEG SEGMENTS OF 80s for resting state and 60s for mental counting. These kinds of data bases are thus excluded by moral and informal qualities and thus these segments are qualified by preprocesses of segments.

Hence, to demonstrate these modules of selection processes are being evaluated to demonstrate them by a single way of modules and hence these modules are selected by each term and their processes are being selected by evaluating them.

C. Proposed Framework

The intention is to make use of this machine to an effort to check the ability of a person. The machine is accountable for gathering aur mind system data related to this machine. The mechanism is based on the nerves of body for locating the best possible outcomes.

Each and every cluster make use of these features which are precise to the framework and the data could be demonstrated within the groups along the machines.

EEGs are regarded as framework-based network- based data and any related data which is employed to differentiate the conditions of a node like a route for displacements, position and attention. The mechanism of EEG models is from a dark sound proof chamber comfortably reclined in an armchair.

In this study we collect and stored the data bases of EEGs recordings using mental health during the last three minutes of the rest period and first two minutes of the mental arithmetic performances. These selected since the task performance strategy is being forms s simultaneously as the task is executed.

Hence, we use the algorithms processes to derive this path and a set of objectives of a process of algorithms. This survey to be accompanied by modules.

IV. DATASET

The database used in this work are taken from Physio net, where healthy subjects were participated. The EEG signals were captured with the help of Neurotome monopolar 23- channel EEG system. Silver/silver chloride electrodes were positioned on the scalp in accordance with the system at central, anterior frontal, frontal, temporal, occipital, and parietal. All the electrodes were assigned to the interconnected ear reference electrodes.

The sample rate for each channel was. A high-pass filter with cut-off frequency, a power line notch filter, a low- pass filter with cut-off frequency were used with a time constant of 0.3 s. Every recording is an EEG segment of 180s for resting phase and 60 s for mental counting phase.

Due to the limited dataset, k-fold cross-validation was used. In our work, through trial and error, we used various amounts of K. We found out that K=10 yields the be results in terms of maximizing the classification performance and minimizing overacting. In each trial, the classification Structure is constructed with 90% of data (80% for the training & 10% for validation) and evaluated with the remaining data as telling data.

In the pandemic situation we observe that about 50% of population in India are occupies with these depression and anxiety modes. Thus, it should be delighted to being in situation that these depressions have a major issue with the control of anxiety and hence its very dangerous for the health. Not only in India but many of the countries have suffering from these issues. So, to understandable these situations the accommodated heathy issues are to be solving by this mechanism.

These are the data sets that are being proving that how the mental growth of people should affect or increasing day by day.

V. EXPERIMENT DESIGN

The dataset was collected to investigate EEG correlates of mental activity during an intensive cognitive task (mental arithmetic task—serial subtraction). This model of research is quite common in the study of the mechanisms of human cognitive activity. Organization of EEG data collection during the experiment. Bounding boxes depict the two EEG recordings stored in the database Arithmetic tasks in this study involved the serial subtraction of two numbers. Each trial started with the oral communication of the 4-digit (minuend) and 2-digit (subtrahend) numbers (e.g., 4753 and 17, 3141 and 42, etc.). Mental arithmetic performance is considered as a standardized stress-inducing experimental protocol. Serial subtraction during 15 min is considered to be a psychosocial stress.

In this way, our study design required intensive cognitive activity from the subjects. Intensive mental load is accompanied by a change in the emotional background when the subject makes additional effort to resolve tasks, so one can talk about evoked emotions in this case.

During EEG recording, the participants sat in a dark soundproof chamber, comfortably reclined in an armchair. Prior to the experiment, participants were instructed to try to relax during the rest state and were informed about the arithmetic task—participants were asked to count mentally without speaking or using finger movements, accurately and quickly, in the rhythm they had determined.

After 3 min of adaptation to experimental conditions, EEG registration of the rest state with closed eyes was made (over the next 3 min). Then the participants performed a mental arithmetic task—serial subtraction—for 4 min. The    course of the experiment is illustrated in Figure 3. The two    bounding boxes represent the data available in the dataset in two separate recording files.

VIII. AUTHOR’S CONTRIBUTIONS

All authors are equally comparable to make this paper. As this researching paper are equally being confront from each other support.

Conclusion

Overall, the understanding and analysis of EEG signals will help a long way in understanding the functioning of the human brain. Recently a lot of in-depth research is being done in this field. The Random Forest vs Decision Tree classifier will help in classifying workload. We have extracted a lot of features and tried them on various types of classifiers. Overall, the model tested on machine Learning algorithm and SVM showed some of the best results and entropy combined with MLPNN had shown an accuracy of 77.72%. PCA tested with MLPNN showed the second-best accuracy of 66.7%. Future work can be done on this problem using transformers for classification and extracting some complex time features.

References

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Copyright

Copyright © 2023 Amit Kumar, Lucky Ram, Radhika Sharma, Tripti Pandey, Prerna Saxena. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.