scholarly journals Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Hamidreza Namazi ◽  
Amin Akrami ◽  
Sina Nazeri ◽  
Vladimir V. Kulish
Keyword(s):  
Brain Research ◽  
Approximate Entropy ◽  
Olfactory Stimulus ◽  
Brain Diseases ◽  
Eeg Signal ◽  
Olfactory Stimuli ◽  
Important Challenge ◽  
Molecular Complexity ◽  
Electroencephalogram Eeg

An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.

AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Research ◽  
Age Groups ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Important Challenge ◽  
Patients With Epilepsy ◽  
Process Complexity ◽  
Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination

2021 ◽  
Author(s):  
Md Samiul Haque Sunny ◽  
Shifat Hossain ◽  
Nashrah Afroze ◽  
Md. Kamrul Hasan ◽  
Eklas Hossain ◽  
...  
Keyword(s):  
Nonlinear Behavior ◽  
Cognitive Status ◽  
Brain Diseases ◽  
Eeg Signal ◽  
Hilbert Huang Transform ◽  
Neuro Fuzzy ◽  
Research Goal ◽  
Adaptive Noise ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Abstract Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.

scholarly journals Fractal Based Analysis of the Influence of Odorants on Heart Activity

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hamidreza Namazi ◽  
Vladimir V. Kulish
Keyword(s):  
Heart Rate ◽  
Heart Diseases ◽  
Approximate Entropy ◽  
Ecg Signal ◽  
Heart Activity ◽  
Heart Research ◽  
Olfactory Stimuli ◽  
Important Challenge ◽  
External Stimuli ◽  
Electrocardiogram Ecg

Abstract An important challenge in heart research is to make the relation between the features of external stimuli and heart activity. Olfactory stimulation is an important type of stimulation that affects the heart activity, which is mapped on Electrocardiogram (ECG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the ECG signal. This study investigates the relation between the structures of heart rate and the olfactory stimulus (odorant). We show that the complexity of the heart rate is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal heart rate. Also, odorant having higher entropy causes the heart rate having lower approximate entropy. The method discussed here can be applied and investigated in case of patients with heart diseases as the rehabilitation purpose.

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi
Keyword(s):  
Time Series ◽  
Biomedical Science ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Olfactory Stimuli ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

scholarly journals A Survey on Machine Learning Algorithms for Vision State Classification and Prediction Through Electroencephalogram (EEG) Signal

2020 ◽  
pp. 426-429
Author(s):  
Devipriya A ◽  
Brindha D ◽  
Kousalya A
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Problem Area ◽  
Machine Learning Algorithms ◽  
Eeg Signal ◽  
Ensemble Machine Learning ◽  
State Classification ◽  
Machine Learning Model ◽  
Knn Classification ◽  
Electroencephalogram Eeg

Eye state ID is a sort of basic time-arrangement grouping issue in which it is additionally a problem area in the late exploration. Electroencephalography (EEG) is broadly utilized in a vision state in order to recognize people perception form. Past examination was approved possibility of AI & measurable methodologies of EEG vision state arrangement. This research means to propose novel methodology for EEG vision state distinguishing proof utilizing Gradual Characteristic Learning (GCL) in light of neural organizations. GCL is a novel AI methodology which bit by bit imports and prepares includes individually. Past examinations have confirmed that such a methodology is appropriate for settling various example acknowledgment issues. Nonetheless, in these past works, little examination on GCL zeroed in its application to temporal-arrangement issues. Thusly, it is as yet unclear if GCL will be utilized for adapting the temporal-arrangement issues like EEG vision state characterization. Trial brings about this examination shows that, with appropriate element extraction and highlight requesting, GCL cannot just productively adapt to time-arrangement order issues, yet additionally display better grouping execution as far as characterization mistake rates in correlation with ordinary and some different methodologies. Vision state classification is performed and discussed with KNN classification and accuracy is enriched finally discussed the vision state classification with ensemble machine learning model.

A COMPUTATIONAL FRAMEWORK TO DISCRIMINATE DIFFERENT ANESTHESIA STATES FROM EEG SIGNAL

2018 ◽  
Vol 30 (03) ◽  
pp. 1850020 ◽  
Author(s):  
Seyyed Abed Hosseini
Keyword(s):  
General Anesthesia ◽  
Rbf Neural Network ◽  
Principal Component ◽  
Approximate Entropy ◽  
Largest Lyapunov Exponent ◽  
Eeg Signal ◽  
Wavelet Coefficients ◽  
Sparse Principal Component Analysis ◽  
Beta Band ◽  
Computational Framework

This paper develops a computational framework to classify different anesthesia states, including awake, moderate anesthesia, and general anesthesia, using electroencephalography (EEG) signal. The proposed framework presents data gathering; preprocessing; appropriate selection of window length by genetic algorithm (GA); feature extraction by approximate entropy (ApEn), Petrosian fractal dimension (PFD), Hurst exponent (HE), largest Lyapunov exponent (LLE), Lempel-Ziv complexity (LZC), correlation dimension (CD), and Daubechies wavelet coefficients; feature normalization; feature selection by non-negative sparse principal component analysis (NSPCA); and classification by radial basis function (RBF) neural network. Because of the small number of samples, a five-fold cross-validation approach is used to validate the results. A GA is used to select that by observing an interval of 2.7[Formula: see text]s for further assessment. This paper assessed superior features, such as LZC, ApEn, PFD, HE, the mean value of wavelet coefficients for the beta band, and LLE. The results indicate that the proposed framework can classify different anesthesia states, including awake, moderate anesthesia, and general anesthesia, with an accuracy of 92.07%, 96.18%, and 93.42%, respectively. Therefore, the proposed framework can discriminate different anesthesia states with an average accuracy of 93.89%. Finally, the proposed framework provided a facilitative representation of the brain’s behavior in different states of anesthesia.

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