Exploring the Potential of EEG Signals in Mental State Analysis | Chapter 1 | Medical Research and Its Applications Vol. 3

 Electroencephalogram (EEG) is a method known as electrophysiology for monitoring and recording the electrical signal activity of the brain. Electroencephalogram (EEG) signals from the human brain have a ground-breaking impact in the field of healthcare industry. EEG is one of the most actively used biosignals as a measurement tool in the Brain-Computer Interface (BCI) to implement “thoughts or intentions” based on human and machine interaction. In the medical field, EEG-based interaction is also used for computer-aided diagnosis for patients and also to help in detecting emotions and body movements. In this paper, the beneficial aspect of EEG is demonstrated which can be a great help for paralysed patients. In this study, especially the consciousness of human beings through analysis of the values of EEG to predict the physical condition of that particular human is observed. The study of characteristics of the signals while the person is moving and when the person intends to move to be diagnosed through BCI can be controlled for humanoid movement through EEG.

 

Author(s)details:-

 

Susmita Das
Electronics and Instrumentation Engineering, Narula Institute of Technology, India.

 

Trisha Paul
Electronics and Instrumentation Engineering, Narula Institute of Technology, India.

 

Chaitali Bhattacharyya
Electronics and Instrumentation Engineering, Narula Institute of Technology, India.

 

Please See the book here :-https://doi.org/10.9734/bpi/mria/v3/3472G

Neurophysiological Commonalities and Differences Across Obsessive- Compulsive, Panic, Phobic and Generalized Anxiety Disorders | Chapter 6 | Advanced Concepts in Medicine and Medical Research Vol. 9

Disorders from anxiety (AD) have been with the leading causes of worldwide health-related burden for over 30 age. Prevalence has increased in spite of strong evidence of effective healing interventions in transverse group studies, partly because of a extreme rate of recurrences and decreased responses in complete individual histories.Comorbidity and pharmacological response among egotistic-compulsive (OCD), collection of stores in one place, social, and specific phobias (SD), panic (PD) and statement anxiety (GAD) disorders desire a single measure: serotonin-dysfunction. Yet, psychiatric classifications gestate those entities as distinct, accompanying strong support from various neuroscience fields.Understanding and target physiopathogenic mechanisms concede possibility improve the long-term healing response, particularly when public, psychological, and biological determinants are combined private affected subjects, stowing differently across individuals, but considerably clustering by nosological individuals.The primary purpose of this phase is to examine for neurophysiological dysfunctions joint by, or different among PD, Dismal, OCD and GAD.A sample state of 192 unmedicated patients and 30 aged-doubled controls partook in this study. Ten liberated factors have included in Theory- related neurophysiological variables. Possibility tables and correspondence analysis75 with u.s. city-square tests were used to describe the sample distribution and connection to clinical groups of the three categorical determinants: EPI, cROI and side (Fig. 1; Table 3), Angler linear discrimination for the all-inclusive ones The nonparametric analysis right classified 81% of the sample. Dysrhythmic patterns, abated delta, and increased testing differentiated AD from controls. Shorter ERP latencies were about several individual patients, generally from the OCD group. Hyperactivities were found at the right front at fixed intervals-striatal network in OCD and at the panic circuit in PD. Our findings support wordy cortical instability in AD in general, accompanying individual differences in information processing losses and regional hyperactivities in OCD and PD. This study judgments suggest that neurophysiology can be used to label ongoing dysfunctions, their relative weights and their interactive patterns on a importance-to-moment base.

Author(s) Details:

Montserrat Gerez,
Department of Clinical Neurophysiology, Hospital Español de México, Mexico City, Mexico and Postgraduate Unit, National Autonomous University of Mexico, Mexico City, Mexico.

Enrique Suárez,
Department of Psychiatry, Hospital Español de México, Mexico City, Mexico and Postgraduate Unit, National Autonomous University of Mexico, Mexico City, Mexico.

Carlos Serrano,
Department of Psychiatry, Hospital Español de México, Mexico City, Mexico and Postgraduate Unit, National Autonomous University of Mexico, Mexico City, Mexico.

Lauro Castanedo,
Department of Psychiatry, Hospital Español de México, Mexico City, Mexico.

Armando Tello,
Department of Clinical Neurophysiology, Hospital Español de México, Mexico City, Mexico and Postgraduate Unit, National Autonomous University of Mexico, Mexico City, Mexico.

Please see the link here: https://stm.bookpi.org/ACMMR-V9/article/view/12850

Electroencephalographic Findings in Patients with Migraine | Chapter 19 | Emerging Trends in Disease and Health Research Vol. 9

Common and incapacitating, migraine is an illness that places a heavy personal strain on patients and a heavy financial load on society. Migraine sufferers are significantly limited in all facets of their everyday life, including job, domestic duties, and leisure activities. A systematic approach to categorization and diagnosis is a crucial prerequisite for clinical care and fruitful research because there are many different illnesses that can cause headaches. Nowadays, individuals with migraine are regularly examined with the Electroencephalogram (EEG). Some migraine sufferers experience high voltage aberrant slow-wave activity. Basilar migraine has been associated with a variety of atypical EEG patterns, such as 1) excessive beta activity during the ictal phase in children, 2) a predominance of delta activity during the headache attack, and 3) normal EEG throughout the assault. 4. Unusual connection of acute confusional state with FIRDA (frontal intermittent rhythmic delta activity) during migraine attack. 3. Slowdown in the posterior area or slowing with spikes and sharp wave complexes 54–56.

Author (s) Details:

Tapaswini Mishra,
Department of Physiology, IMS & SUM Hospital, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.

Dipti Mohapatra,
Department of Physiology, IMS & SUM Hospital, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.

Priyambada Panda,
Department of Physiology, IMS & SUM Hospital, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.

Arati Meher,
Department of Physiology, IMS & SUM Hospital, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.

Ellora Devi,
Department of Physiology, IMS & SUM Hospital, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.

Please see the link here: https://stm.bookpi.org/ETDHR-V9/article/view/7457

Study on Seizure Detection from the Features of EEG Signals | Chapter 8 | New Approaches in Engineering Research Vol. 16

 A seizure must be identified in order to support an epileptic patient's diagnosis and treatment. The goal of this study is to use an EEG signal to automatically detect epileptic episodes in a patient. The EEG signal is proven to be more favourable than other biological signals such as PET, MEG, MRI, and fMRI. The EEG signal that was recorded was first preprocessed. The EEG signal's features were then determined, and the signal was then classed as seizure or normal based on the calculated features. The highest performing characteristics were chosen from a comparison of features such as Mean, PSE (Power Spectral Entropy), variance, and energy. To confirm a robust feature vector, weighted combinations of these characteristics were obtained. We suggest a weighted mixture of variance and energy (in two specific frequency bands) as a composite characteristic in this research. We established a threshold for this composite feature, using which an EEG signal may be categorised as normal or seizure-like. The recommended feature composition provides up to 96.5 percent accuracy.

Author(S) Details

Anita Patil
Department of Electronics and Tele-Communication, Cummins College of Engineering for Women, Pune, India.

View Book:- https://stm.bookpi.org/NAER-V16/article/view/4061

Metrological Research of Methods and Means of EEG Analysis in Science and Medicine | Book Publisher International

 This small monograph contains in-depth critical examinations on four EEG analysis methods: 1) the disadvantages and errors of coherent analysis; 2) errors in spectral estimates of EEG amplitude compared to direct amplitude measurements; 3) a new method for analysing EEG synchronicity using envelopes correlation and demonstrating its effectiveness in differentiating norm and schizophrenia; 4) the final solution to the problem of selecting the best reference electrode for EEG recording.

Author(S) Detalis

A. P. Kulaichev
Department of Biology, Moscow State University, Moscow, 119234, Russia.

View Book:- https://stm.bookpi.org/MRMMEASM/article/view/3427

The Effect of Alpha Oscillation Network Decoding on Driver Alertness | Chapter 10 | Newest Updates in Physical Science Research Vol. 9

 This research describes a novel way to employing artificial neural networks (ANNs) to improve transmission line protection. The suggested technique feeds four different neural network structures instantaneous voltages and currents on a transmission line during normal and fault conditions. The structures are then expertly merged to provide a system that can more effectively detect and diagnose shunt problems. The report goes into great detail about the design process as well as the many simulations that were run. The accuracy and mean square error (MSE) of the created system are examined, and the findings reveal that this approach is capable of identifying and classifying all probable shunt faults on the 33-kV Nigeria power lines in less than 1ms with a high level of precision. When evaluated under various shunt fault types with varying resistances and distances, the system's performance demonstrates that it can be used to improve distance line protection in 33-kV Nigeria power lines.

Author (s) Details

Chi Zhang
Faculty of Electronic Information and Electrical Engineering, School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.

Jinfei Ma
School of Psychology, Liaoning Normal University, Dalian 116029, China.

Jian Zhao
Faculty of Vehicle Engineering and Mechanics, School of Automative Engineering, Dalian University of Technology, Dalian 116024, China.

Pengbo Liu
Faculty of Vehicle Engineering and Mechanics, School of Automative Engineering, Dalian University of Technology, Dalian 116024, China.

Fengyu Cong
Faculty of Electronic Information and Electrical Engineering, School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China and School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China and Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, Dalian, China and Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland.

Tianjiao Liu
School of Psychology, Shandong Normal University, Jinan 250358, China.

Ying Li
Faculty of Electronic Information and Electrical Engineering, School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.

Lina Sun
Faculty of Electronic Information and Electrical Engineering, School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.

Ruosong Chang
School of Psychology, Liaoning Normal University, Dalian 116029, China.

View Book :- https://stm.bookpi.org/NUPSR-V9/article/view/1941

Application of EEG Signals – A Case Study | Chapter 10 | Advanced Aspects of Engineering Research Vol. 11

 Traditional iterative estimate methods are replaced by the Gauss-Jacobi combinatorial algorithm. In nonlinear models, where other parameter estimate approaches fail, the combinatorial approach is frequently employed for outlier diagnosis. The purpose of this study is to compare the effectiveness of the Gauss-Jacobi and Gauss-Markov models when used to the parameter estimation process of a levelling network for the purpose of determining the efficiency of a combinatorial algorithm in a simple linear model. The Man-Machine-Interface (MMI) is a communication device that connects the brain to a computer in order to obtain and analyse brain data. The electrical signals produced by nerve cells are captured by the EEG. The goal of this work is to present the findings of EEG signal categorization and the use of appropriate music to represent different people's emotions based on their emotions. The study was carried out on a dataset that included 10 people who were exposed to patriotic, joyful, romantic, and sad songs, as well as relaxing activities.

Author (s) Details

Guruprasad S.
BMS Institute of Technology & Management, Bengaluru, Karnataka, India.

Veena N.
BMS Institute of Technology & Management, Bengaluru, Karnataka, India.

S. Mahalakshmi
BMS Institute of Technology & Management, Bengaluru, Karnataka, India.

View Book : https://stm.bookpi.org/AAER-V11/article/view/1255

Consumer BCI Devices - Applications and Challenges | Chapter 8 | Advanced Aspects of Engineering Research Vol. 11

 The Brain-Computer-Interface (BCI) is a system that collects, analyses, and converts brain signals into commands that can assist humans in controlling their surroundings. BCI is employed in a variety of applications, including security, medical, games, self-regulation, and research. Using various Electroencephalography (EEG) devices, BCI provides a mutual link between the brain and the outside environment. Many people are paralysed today, and these EEG devices can assist them in communicating with the outside world. This chapter will look at different EEG devices with varying numbers of electrodes, their benefits and drawbacks, and applications that will benefit society in the long term.

Author (s) Details

Veena N.
Information Science and Engineering, BMS Institute of Technology & Management (BMSIT&M), Bengaluru, India.

S. Mahalakshmi
Information Science and Engineering, BMS Institute of Technology & Management (BMSIT&M), Bengaluru, India.

Guruprasad S.
Computer Science and Engineering, BMS Institute of Technology & Management (BMSIT&M), Bengaluru, India.

View Book : https://stm.bookpi.org/AAER-V11/article/view/1253

Study of Electroencephalography for Enhanced Understanding of Consumer Preference | Chapter 12 | New Ideas Concerning Science and Technology Vol. 8

Consumer response to food items has traditionally been measured using consumer recorded thoughts or questionnaires, which may be subject to cognitive bias. Food decisions are informed by a diverse collection of thoughts, feelings, behaviours, and beliefs that are difficult to determine simply by polling consumers. Electroencephalography (EEG) is an electrophysiological tool that can provide tacit and detailed data for an unbiased approach. Consumers' perceptive, attentive, and emotional processes against foods are recorded and explained using electroencephalography (EEG). To further this understanding, the field of "neuromarketing," which employs neuroscientific techniques to research consumer behaviour, has recently gained popularity. In a stimulated human, the asymmetry of the EEG signal between the right and left hemispheres of the brain can be used to assess the acceptability of stimuli. EEG allows advertisers to compare customer responses to various marketing stimuli and effect moments associated with a specific product or brand in order to better place the product in the market.

Author (s) Details

B. Neeharika
PGRC, PJTSAU Rajendranagar, Hyderabad – 500 030, India.

W. Jessie Suneetha
Krishi Vigyan Kendra, PJTSAU, Wyra 507165, Khammam Dt., India.

B. Anila Kumari
PGRC, PJTSAU Rajendranagar, Hyderabad – 500 030, India.

M. Tejashree
College of Agriculture, PJTSAU, Rajendranagar, Hyderabad – 500 030, India.

View Book :- https://stm.bookpi.org/NICST-V8/issue/view/45