scholarly journals Novel Approach for EEG Signal Analysis in a Multifractal Paradigm of Motions. Epileptic and Eclamptic Seizures as Scale Transitions

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1024
Author(s):  
Stefan Andrei Irimiciuc ◽  
Andrei Zala ◽  
Dan Dimitriu ◽  
Loredana Maria Himiniuc ◽  
Maricel Agop ◽  
...  
Keyword(s):  
Brain Activity ◽  
Relativity Theory ◽  
Harmonic Mappings ◽  
Eeg Signal ◽  
Neuronal Dynamics ◽  
Novel Approach ◽  
Dynamic Methods ◽  
Time Variations ◽  
Scale Relativity ◽  
The Brain

Two different operational procedures are proposed for evaluating and predicting the onset of epileptic and eclamptic seizures. The first procedure analyzes the electrical activity of the brain (EEG signals) using nonlinear dynamic methods (the time variations of the standard deviation, the variance, the skewness and the kurtosis; the evolution in time of the spatial–temporal entropy; the variations of the Lyapunov coefficients, etc.). The second operational procedure reconstructs any type of EEG signal through harmonic mappings from the usual space to the hyperbolic one using the time homographic invariance of a multifractal-type Schrödinger equation in the framework of the scale relativity theory (i.e., in a multifractal paradigm of motions). More precisely, the explicit differential descriptions of the brain activity in the form of 2 × 2 matrices with real elements disclose, through the in-phase coherences at various scale resolutions (i.e., as scale transitions), the multitude of brain neuronal dynamics, especially sequences of epileptic and eclamptic seizures. These two operational procedures are not mutually exclusive, but rather become complementary, offering valuable information concerning epileptic and eclamptic seizures. In such context, the prediction of epileptic and eclamptic seizures becomes fundamental for patients not responding to medical treatment and also presenting an increased rate of seizure recurrence.

scholarly journals Novel Approach for EKG Signals Analysis Based on Markovian and Non-Markovian Fractalization Type in Scale Relativity Theory

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 456
Author(s):  
Maricel Agop ◽  
Stefan Irimiciuc ◽  
Dan Dimitriu ◽  
Cristina Marcela Rusu ◽  
Andrei Zala ◽  
...  
Keyword(s):  
Heart Diseases ◽  
Signal Reconstruction ◽  
Relativity Theory ◽  
Harmonic Mappings ◽  
Disease Evolution ◽  
Electrocardiogram Signals ◽  
Novel Approach ◽  
Dynamic Methods ◽  
Scale Relativity ◽  
Second Procedure

Two distinct operational procedures are proposed for diagnosis and tracking of heart disease evolution (in particular atrial fibrillations). The first procedure, based on the application of non-linear dynamic methods (strange attractors, skewness, kurtosis, histograms, Lyapunov exponent, etc.) analyzes the electrical activity of the heart (electrocardiogram signals). The second procedure, based on multifractalization through Markovian and non-Markovian-type stochasticizations in the framework of the scale relativity theory, reconstructs any type of EKG signal by means of harmonic mappings from the usual space to the hyperbolic one. These mappings mime various scale transitions by differential geometries, in Riemann spaces with symmetries of SL(2R)-type. Then, the two operational procedures are not mutually exclusive, but rather become complementary, through their finality, which is gaining valuable information concerning fibrillation crises. As such, the author’s proposed method could be used for developing new models for medical diagnosis and evolution tracking of heart diseases (patterns dynamics, signal reconstruction, etc.).

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.

scholarly journals Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

2020 ◽  
Author(s):  
Z. Zavecz ◽  
K. Janacsek ◽  
P. Simor ◽  
M.X. Cohen ◽  
D. Nemeth
Keyword(s):  
Functional Connectivity ◽  
Memory Consolidation ◽  
Brain Activity ◽  
Serial Order ◽  
Order Information ◽  
Novel Approach ◽  
Continuous Eeg ◽  
Induced Changes ◽  
Probability Information ◽  
The Brain

AbstractLong-term memory depends on memory consolidation that seems to rely on learning-induced changes in the brain activity. Here, we introduced a novel approach analyzing continuous EEG data to study learning-induced changes as well as trait-like characteristics in brain activity underlying consolidation. Thirty-one healthy young adults performed a learning task and their performance was retested after a short (~1h) delay, that enabled us to investigate the consolidation of serial-order and probability information simultaneously. EEG was recorded during a pre- and post-learning rest period and during learning. To investigate the brain activity associated with consolidation performance, we quantified similarities in EEG functional connectivity of learning and pre-learning rest (baseline similarity) as well as learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes in similarity from baseline to post-learning could indicate learning-induced changes, possibly spontaneous reactivation. Individuals with higher learning-induced changes in alpha frequency connectivity (8.5–9.5 Hz) showed better consolidation of serial-order information. This effect was stronger for more distant channels, highlighting the role of long-range centro-parietal networks underlying the consolidation of serial-order information. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5–3 Hz) and seemed to be dependent on more local, short-range connections. Beyond these associations with learning-induced changes, we also found substantial overlap between the baseline and post-learning similarity and their associations with consolidation performance, indicating that stable (trait-like) differences in functional connectivity networks may also be crucial for memory consolidation.Significance statementWe studied memory consolidation in humans by characterizing how similarity in neural oscillatory patterns during learning and rest periods supports consolidation. Previous studies on similarity focused on learning-induced changes (including reactivation) and neglected the stable individual characteristics that are present over resting periods and learning. Moreover, learning-induced changes are predominantly studied invasively in rodents or with neuroimaging or event-related electrophysiology techniques in humans. Here, we introduced a novel approach that enabled us 1) to reveal both learning-induced changes and trait-like individual differences in brain activity and 2) to study learning-induced changes in humans by analyzing continuous EEG. We investigated the consolidation of two types of information and revealed distinct learning-induced changes and trait-like characteristics underlying the different memory processes.

DECODING OF SIMPLE AND COMPOUND LIMB MOTOR IMAGERY MOVEMENTS BY FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAM (EEG) SIGNAL

Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950041 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
TIRDAD SEIFI ALA
Keyword(s):  
Fractal Dimension ◽  
Motor Imagery ◽  
Fractal Analysis ◽  
Brain Activity ◽  
Eeg Signal ◽  
Left Hand ◽  
Right Hand ◽  
Significant Difference ◽  
Electroencephalogram Eeg ◽  
The Brain

One of the major attempts in rehabilitation science is to decode different movements of human using physiological signals. Since human movements are mainly controlled by the brain, decoding of movements by analysis of the brain activity has great importance. In this paper, we apply fractal analysis to Electroencephalogram (EEG) signal in order to decode simple and compound limb motor imagery movements. The fractal dimension of EEG signal is analyzed in case of left hand, right hand, both hands, feet, left hand combined with right foot, and right hand combined with left foot movements. Based on the obtained results, EEG signal experiences the lowest and greatest fractal dimension in case of both hands movement, and feet movement, respectively. Besides obtaining different fractal dimension for EEG signal in case of different movements, no significant difference was observed in fractal dimension of EEG signal between different movements. The method of analysis employed in this research can be widely applied to analysis of EEG signal for decoding of different movements of human.

Application of the Hilbert_Huang Transform and Wavelet Transform in the EEG Processing

2013 ◽  
Vol 756-759 ◽  
pp. 1753-1757
Author(s):  
Gui Xin Zhang ◽  
Ping Dong Wu ◽  
Man Ling Huang
Keyword(s):  
Wavelet Transform ◽  
Brain Activity ◽  
Eeg Signal ◽  
Hilbert Huang Transform ◽  
Signal Processing Method ◽  
Communication Method ◽  
Machine Interface ◽  
And Control ◽  
The Brain ◽  
Eeg Signal Processing

Brain-Machine Interface (BMI) could make people control machine through EEG which is produced by the brain activity, and it provide a new communication method between human and machine. The research for BMI will extend the ability of communication and control the environment and machine. The key point of the BMI is how to abstract and distinguish different EEG characters. Therefore, EEG signal processing method is the emphasis of BMI. Wavelet Transform and Hilbert-Huang Transform are used to analyze the EEG signal in this paper. The results indicate that these two methods could abstract the main characters of the EEG, but the Hilbert-Huang Transform could express the distributing status in the time and frequency aspect of the EEG more accurately, because it produces the self-adaptive basis according the data, and obtain the local and instantaneous frequency of the EEG.

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.

scholarly journals A Robust Emotion Extraction System from EEG Signal Dataset using Machine Learning

2021 ◽  
pp. 319-330
Author(s):  
Muthulakshmi P ◽  
Gopika R
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Extraction System ◽  
Eeg Signal ◽  
Eeg Asymmetry ◽  
Non Invasive ◽  
Brain Signals ◽  
Human Stress ◽  
Invasive Method ◽  
The Brain

The project entitled “A Robust Emotion Extraction System from EEG signal Dataset using Machine Learning” has been developed using MATLAB. The brain activity produces the different kinds of signals like electrical and magnetic signals. This activity can be recorded using different kind of approaches, which are normally classified as invasive and non-invasive. In invasive methods surgical intervention are made to implant certain device in the brain whereas in non-invasive methods no such intervention is made. Among the different non-invasive methods, Electroencephalography is one of the most commonly used methods to record the brain signals. EEG is regarded as direct and simple non-invasive method to record the brain electrical activity. Current flow in the neurons of the brain is represented as voltage fluctuation (EEG). EEG waves which can be represented as the signal over time are recorded by the electrodes places on scalp over the brain. EEG Asymmetry and Spectral Centroids techniques in extracting unique features for human stress. In our proposed work we have to classify the EEG signal whether that is stress or not. In our proposed work we will extract the features and optimizing Using Genetic Algorithm then we finally classify the EEG signal.

scholarly journals Motor imagery recognition in electroencephalograms using convolutional neural networks

2020 ◽  
Vol 44 (3) ◽  
pp. 482-487
Author(s):  
A.D. Bragin ◽  
V.G. Spitsyn
Keyword(s):  
Neural Networks ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Activity ◽  
Processing Unit ◽  
Eeg Signal ◽  
Good Tool ◽  
Markov Transition ◽  
Transition Field ◽  
The Brain

Electroencephalography is a widespread method to record brain signals with the use of electrodes located on the surface of the head. This method of recording the brain activity has become popular because it is relatively cheap, compact, and does not require implanting the electrodes directly into the brain. The article is devoted to a problem of recognition of motor imagery by electroencephalogram signals. The nature of such signals is complex. Characteristics of electroencephalograms are individual for every person, also depending on their age and mental state, as well as the presence of noise and interference. The multitude of these parameters should be taken into account when analyzing encephalograms. Artificial neural networks are a good tool for solving this class of problems. Their application allows combining the tasks of extracting, selecting and classifying features in one signal processing unit. Electroencephalograms are time signals and we note that Gramian Angular Fields and Markov Transition Field transforms are used to represent time series in the form of images. The article shows the possibility of using the Gramian Angular Fields and Markov Transition Field transformations of the electroencephalogram (EEG) signal for motor imagery recognition using examples of imaginary movements with the right and left hand, also studying the effect of the resolution of Gramian Angular Fields and Markov Transition Field images on the classification accuracy. The best classification accuracy of the EEG signal into the motion and state-of-rest classes is about 99%. In future, the research results can be applied in constructing the brain-computer interface.

Research on Processing the Brain Activity in BCI System

2017 ◽  
pp. 152-178
Author(s):  
Jaromir Svejda ◽  
Roman Zak ◽  
Roman Senkerik ◽  
Roman Jasek
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Research Direction ◽  
Basic Knowledge ◽  
Future Research ◽  
Eeg Signal ◽  
Practical Applications ◽  
Output Device ◽  
The Brain ◽  
Bci System

The basic idea of BCI (Brain Computer Interface) is to connect brain waves with an output device through some interface. Human brain activity can be measured by many technologies. In our research, we use EEG (Electroencephalography) technology. This chapter will deal with processing of EEG signal and its utilization in practical applications using BCI technology mentioned above. This chapter is organized as follows. Firstly, the basic knowledge about EEG technology, brain and biometry is briefly summarized. Secondly, research of authors is presented. Finally, the future research direction is mentioned.

FRACTAL-BASED ANALYSIS OF THE INFLUENCE OF VARIATIONS OF RHYTHMIC PATTERNS OF MUSIC ON HUMAN BRAIN RESPONSE

Fractals ◽  
2018 ◽  
Vol 26 (05) ◽  
pp. 1850080 ◽  
Author(s):  
ZHALEH MOHAMMAD ALIPOUR ◽  
REZA KHOSROWABADI ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Human Brain ◽  
Human Behavior ◽  
Fractal Structure ◽  
Brain Activity ◽  
Chaotic Signal ◽  
Auditory Stimuli ◽  
Eeg Signal ◽  
Brain Response ◽  
Human Eeg ◽  
The Brain

Analysis of human behavior is one of the major research topics in neuroscience. It is known that human behavior is related to his brain activity. In this way, the analysis of human brain activity is the root for analysis of his behavior. Electroencephalography (EEG) as one of the most famous methods for measuring of the brain activity generates a chaotic signal, which has fractal characteristic. This study reveals the relation between the fractal structure (complexity) of human EEG signal and the applied auditory stimuli. For this purpose, we chose a range of auditory stimuli with different rhythmic patterns. We demonstrated that the fractal structure of human EEG signal changes significantly based on different rhythmic patterns. The capability observed in this research can be applied to other kinds of stimuli in order to classify the brain response based on the types of stimuli.

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