USING A SIXTEEN-CHANNEL EEG RECORDING COMPLEX TO DETECT VARIOUS CHANGES IN THE ELECTRICAL ACTIVITY OF THE BRAIN FOR FURTHER INTERPRETATION

2021 ◽  
pp. 70-78
Author(s):  
Д.В. Журавлёв ◽  
А.А. Проводников
Keyword(s):  
Electronic Device ◽  
Brain Activity ◽  
Radio Channel ◽  
Algebraic Function ◽  
Eeg Signals ◽  
Software Complex ◽  
Subsequent Processing ◽  
Mathematical Processing ◽  
The Brain ◽  
Control Samples

Проведена апробация изготовленного по материалам открытого проекта шестнадцатиканального мобильного комплекса регистрации электроэнцефалограммы (ЭЭГ). Аппаратно-программный комплекс регистрации ЭЭГ позволяет проводить регистрацию неинвазивным способом 16-ти монополярных ЭЭГ каналов, содержащих биоэлектрические сигналы головного мозга человека. Все составные элементы комплекса регистрации конструктивно расположены на шлеме-основе из твердого пластика. Шлем надевается на голову и удерживает на себе до 32-х вкручивающихся штырьковых электродов, платы электронного устройства регистрации и обработки сигналов, радиопередатчики, аккумуляторные батареи. Регистрируемые сигналы ЭЭГ в режиме реального времени передаются по радиоканалу (стандарт Wi-Fi) на ЭВМ для последующей обработки. Сигналы ЭЭГ, полученные в ЭВМ, подаются в пакет прикладных программ MATLAB для последующей обработки. Сигналы ЭЭГ в ЭВМ формируются в виде стандартных цифровых отсчетов и, соответственно, могут быть переданы в любую программу обработки данных. Сигналы ЭЭГ должны быть подвергнуты математической обработке для выявления определенных состояний головного мозга и формирования паттернов ЭЭГ, служащих ориентирами при подготовке управляющих сигналов на внешние исполнительные устройства. При математической обработке полученных сигналов был проведен анализ частотного состава ЭЭГ, проведены специальные преобразования сигналов и вспомогательные операции для идентификации необходимых паттернов ЭЭГ сигналов. В первую очередь была проведена фильтрация полученных сигналов полосовым фильтром и алгебраической функцией вейвлета Добеши 8-го уровня. Затем были собраны контрольные образцы мозговой деятельности при выполнении трех типов активностей. Обнаружена корреляция между экспериментами и контрольными образцами. Сделанные наработки могут быть использованы для упрощения установки входных параметров искусственных нейронных сетей, применяемых для обработки и анализа сигналов ЭЭГ We carried out the approbation of a sixteen-channel mobile EEG registration complex made based on the materials of an open project. The hardware and software complex for EEG registration allows for non-invasive registration of 16 monopolar EEG channels containing bioelectric signals of the human brain. All the components of the registration complex are structurally located on a helmet-based made of hard plastic. The helmet is put on the head and holds up to 32 screw-in pin electrodes, boards of an electronic device for recording and processing signals, radio transmitters, and batteries. The recorded EEG signals are transmitted in real time via a radio channel (Wi-Fi standard) on a computer for subsequent processing. The EEG signals received in the computer are fed into the MATLAB application software package for subsequent processing. The EEG signals in the computer are formed in the form of standard digital samples and, accordingly, can be transmitted to any data processing program. EEG signals should be subjected to mathematical processing to identify certain states of the brain and form EEG patterns that serve as guidelines for the preparation of control signals to external actuators. During the mathematical processing of the received signals, we analyzed the frequency composition of the EEG, special signal transformations and performed auxiliary operations to identify the necessary EEG signal patterns. First of all, we filtered the received signals by a bandpass filter and an algebraic function of the Daubechy wavelet of the 8th level. Then, we collected control samples of brain activity when performing three types of activities. We found a correlation between the experiments and the control samples. It can be developed to be used to simplify the installation of input parameters of artificial neural networks used for processing and analyzing EEG signals

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Information-based decoding of the coupling among human brain activity and movement paths

2021 ◽  
pp. 1-10
Author(s):  
Shahul Mujib Kamal ◽  
Norazryana Mat Dawi ◽  
Hamidreza Namazi
Keyword(s):  
Brain Activity ◽  
Point Of View ◽  
Physiological Signals ◽  
Human Walking ◽  
Eeg Signals ◽  
Rehabilitation Science ◽  
Information Contents ◽  
First Time ◽  
The Brain ◽  
Brain Reaction

BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements. OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view. METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents. RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

Information-based Analysis of the Coupling between Dynamic Visual Stimuli, Eye Movements, and Brain Signals

2021 ◽  
pp. 2150048
Author(s):  
Hamidreza Namazi ◽  
Avinash Menon ◽  
Ondrej Krejcar
Keyword(s):  
Eye Movements ◽  
Moving Objects ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Vision Science ◽  
Brain Signals ◽  
Information Contents ◽  
The Brain

Our eyes are always in search of exploring our surrounding environment. The brain controls our eyes’ activities through the nervous system. Hence, analyzing the correlation between the activities of the eyes and brain is an important area of research in vision science. This paper evaluates the coupling between the reactions of the eyes and the brain in response to different moving visual stimuli. Since both eye movements and EEG signals (as the indicator of brain activity) contain information, we employed Shannon entropy to decode the coupling between them. Ten subjects looked at four moving objects (dynamic visual stimuli) with different information contents while we recorded their EEG signals and eye movements. The results demonstrated that the changes in the information contents of eye movements and EEG signals are strongly correlated ([Formula: see text]), which indicates a strong correlation between brain and eye activities. This analysis could be extended to evaluate the correlation between the activities of other organs versus the brain.

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

scholarly journals DEVELOPMENT OF THE HARDWARE AND SOFTWARE COMPLEX CONTROLLING ROBOTIC DEVICES BY MEANS OF BIOELECTRIC SIGNALS OF THE BRAIN AND MUSCLES

2016 ◽  
Vol 1 (3) ◽  
pp. 77-82
Author(s):  
S Yu Gordleeva ◽  
S A Lobov ◽  
V I Mironov ◽  
I A Kastalskiy ◽  
M V Lukoyanov ◽  
...  
Keyword(s):  
Software System ◽  
Effort Estimation ◽  
Eeg Signals ◽  
Proportional Control ◽  
Software Complex ◽  
Emg Patterns ◽  
Robotic Devices ◽  
Bioelectric Signals ◽  
The Brain ◽  
Speed And Accuracy

Aim - to develop a hardware-software complex with combined command-proportional control of robotic devices based on electromyography (EMG) and electroencephalography (EEG) signals. Materials and methods. EMG and EEG signals are recorded using our original units. The system also supports a number of commercial EEG and EMG recording systems, such as NVX52 (MCS ltd, Russia), DELSYS Trigno (Delsys Inc, USA), MYO Thalmic (Thalmic Labs, Canada). Raw signals undergo preprocessing and feature extraction. Then features are fed to classifiers. The interpretation unit controls robotic devices on the base of classified EEG- and EMG-patterns and muscle effort estimation. The number of controlled devices includes mobile robot LEGO NXT Mindstorms (LEGO, Denmark), humanoid robot NAO (Aldebaran, France) and exoskeleton Ilia Muromets (UNN, Russia). Results. We have developed and tested an interface combining command and proportional control based on EMG signals. We have determined the parameters providing optimal characteristics of classification accuracy of EMG patterns, as well as the speed and accuracy of proportional control. Also we have developed and tested a BCI interface based on motor imagined patterns. Both EMG and EEG interfaces are included into hardware and software system. The system combines outputs of the interfaces and sends commands to a robotic device. Conclusion. We have developed and approved the hardware-software system on the basis of the combined command-proportional EMG and EEG control of external robotic devices.

MONOTONOUS TASKS AND ALCOHOL CONSUMPTION EFFECTS ON THE BRAIN BY EEG ANALYSIS USING NEURAL NETWORKS

2012 ◽  
Vol 11 (03) ◽  
pp. 1250015 ◽  
Author(s):  
STEPHEN KARUNGARU ◽  
TOSHIHIRO YOSHIDA ◽  
TORU SEO ◽  
MINORU FUKUMI ◽  
KENJI TERADA
Keyword(s):  
Neural Networks ◽  
Mental Stress ◽  
Brain Activity ◽  
Principal Component ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
The Relationship ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Eeg Signal Analysis

An analysis of the Electroencephalogram (EEG) signals while performing a monotonous task and drinking alcohol using principal component analysis (PCA), linear discriminant analysis (LDA) for feature extraction and Neural Networks (NNs) for classification is proposed. The EEG is captured while performing a monotonous task that can adversely affect the brain and possibly cause stress. Moreover, we investigate the effects of alcohol on the brain by capturing the data continuously after consumption of equal amounts of alcohol. We hope that our work will shed more light on the relationship between such actions and EEG, and investigate if there is any relation between the tasks and mental stress. EEG signals offers a rare look at brain activity, while, monotonous activities are well known to cause irritation which may contribute to mental stress. We apply PCA and LDA to characterize the change in each component, extract it and discriminate using a NN. After experiments, it was found that PCA and LDA are effective analysis methods in EEG signal analysis.

COMPLEXITY-BASED ANALYSIS OF BRAINS’ SYNCHRONIZATION IN HUMAN–HUMAN INTERACTION

Fractals ◽  
2020 ◽  
Vol 28 (07) ◽  
pp. 2050102 ◽  
Author(s):  
MOHAMED RASMI ASHFAQ AHAMED ◽  
MOHAMMAD HOSSEIN BABINI ◽  
NAJMEH PAKNIYAT ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Fractal Analysis ◽  
Brain Activity ◽  
Human Interaction ◽  
Physiological Signals ◽  
Eeg Signals ◽  
Human Voice ◽  
The Mean ◽  
First Time ◽  
The Relationship ◽  
The Brain

Talking is the most common type of human interaction that people have in their daily life. Besides all conducted studies on the analysis of human behavior in different conditions, no study has been reported yet that analyzed how the brain activity of two persons is related during their conversation. In this research, for the first time, we investigate the relationship between brain activities of people while communicating, considering human voice as the mean of this connection. For this purpose, we employ fractal analysis in order to investigate how the complexity of electroencephalography (EEG) signals for two persons are related. The results showed that the variations of complexity of EEG signals for two persons are correlated while communicating. Statistical analysis also supported the result of analysis. Therefore, it can be stated that the brain activities of two persons are correlated during communication. Fractal analysis can be employed to analyze the correlation between other physiological signals of people while communicating.

scholarly journals Translation of Brain Activity Patterns of a user into Commands using Electroencephalography (EEG)

2021 ◽  
pp. 62-68
Author(s):  
Ranjana B. Jadekar ◽  
A. R. Sindhu ◽  
M. T. Vinay
Keyword(s):  
Environmental Control ◽  
Brain Activity ◽  
Activity Patterns ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Control Option ◽  
Electrophysiological Signals ◽  
Interactive Application ◽  
The Brain ◽  
Bci System

Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). The BCI system uses oscillatory Electroencephalography (EEG) signals, recorded using specific mental activity, as input and provides a control option by its output. A brain-computer interface uses electrophysiological signals to control the remote devices. They consist of electrodes applied to the scalp of an individual or worn in an electrode cap. The computer processes the EEG signals and uses it in order to accomplish tasks such as communication and environmental control.

scholarly journals Best Feature Extraction and Classification Algorithms for EEG Signals in Neuromarketing

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Boniadi Naieni
Keyword(s):  
Feature Extraction ◽  
Brain Activity ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
High Efficient ◽  
The Brain

Purpose: There are many methods for advertisements of products and neuromarketing is new area in this field. In neuromarketing, we use neuroscience information for revealing Consumer behavior by extracting brain activity. Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG), and Electroencephalography (EEG) are high efficient tools for investigating the brain activity in neuromarketing. EEG signal is a high temporal resolution and a cheap method for examining the brain activity. Materials and Methods: 32 subjects (16 males and 16 females) aging between 20-35 years old participated in this study. We proposed neuromarketing method exploit EEG system for predicting consumer preferences while they view E-commerce products. We apply some important preprocessing steps for noise and artifacts elimination of the EEG signal. In next step feature extraction methods are applied on the EEG data such as Discrete Wavelet Transform (DWT) and statistical features. The goal of this study is classification of analyzed EEG signal to likes and dislikes using supervised algorithms. We use Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) for data classification. The mentioned methods were used for whole and lobe brain data. Results: The results show high efficacy for SVM algorithms than other methods. Accuracy, sensitivity, specificity and precision parameters were used for evaluation of the model performance. The results show high performance of SVM algorithms for classification of the data with accuracy more than 87% and 84% for whole and parietal lobe data. Conclusion: We designed a tool with EEG signals for extraction brain activity of consumers using neuromarketing methods. We investigated the effects of advertising on brain activity of consumers by EEG signals measures.

Information-Based Classification of Electroencephalography (EEG) Signals for Healthy Adolescents and Adolescents with Symptoms of Schizophrenia

2020 ◽  
Vol 19 (04) ◽  
pp. 2050033 ◽  
Author(s):  
Hamidreza Namazi
Keyword(s):  
Information Content ◽  
Shannon Entropy ◽  
Healthy Subjects ◽  
Brain Activity ◽  
Research Area ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
First Time ◽  
The Brain

Analysis of the brain activity is the major research area in human neuroscience. Besides many works that have been conducted on analysis of brain activity in case of healthy subjects, investigation of brain activity in case of patients with different brain disorders also has aroused the attention of many researchers. An interesting category of works belong to the comparison of brain activity between healthy subjects and patients with brain disorders. In this research, for the first time, we compare the brain activity between adolescents with symptoms of schizophrenia and healthy subjects, by information-based analysis of their Electroencephalography (EEG) signals. For this purpose, we benefit from the Shannon entropy as the indicator of information content. Based on the results of analysis, EEG signal in case of healthy subjects contains more information than EEG signal in case of subjects with schizophrenia. The result of statistical analysis showed the significant variation in the Shannon entropy of EEG signal between healthy adolescents and adolescents with symptoms of schizophrenia in case of P3, O1 and O2 channels. The employed method of analysis in this research can be further extended in order to investigate the variations in the information content of EEG signal in case of subjects with other brain disorders versus healthy subjects.

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