Artificial EEG signal generated by a network of neurons with one and two dendrites
The electroencephalogram EEG signal analysis is being strongly explored as a new potential tool for control, communication, and clinical diagnosis applications related to many neurological pathologies such as epilepsy, autism, Alzheimer. Analyzing EEG data is a very interesting approach to study cog...
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| Published in | Results in physics Vol. 20; p. 103699 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.01.2021
Elsevier |
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| ISSN | 2211-3797 2211-3797 |
| DOI | 10.1016/j.rinp.2020.103699 |
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| Abstract | The electroencephalogram EEG signal analysis is being strongly explored as a new potential tool for control, communication, and clinical diagnosis applications related to many neurological pathologies such as epilepsy, autism, Alzheimer. Analyzing EEG data is a very interesting approach to study cognitive processes. Thus, it can help researchers to understand the brain processes, doctor to establish an efficient medical diagnosis. To better understand the nature and the behavior of this signal which seems to be complex, nonlinear, non-stationary, and imbalanced, we propose to generate this signal using a predefined model of neurons with one and two dendrites by studying their activation functions. Then, we build a network of neurons with two dendrites correlated with the α and β frequencies which represent the most significant sub-bands in EEG signals to generate a signal quite close to EEG recordings. Through this paper, we aim to present a comprehensive study and an exploration of artificial EEG signal generation using only neurons with two dendrites that provided different activation and excitation functions. The presented result shows that the generated EEG signal with six neurons of two dendrites is quite similar to the real EEG recording for a healthy subjects. In addition, some artifacts like Alzheimer and Parkinson have been emulated and identified among the generated spikes. Finally we proved that we can eliminate some artifacts related to harmonics by applying activation function with opposite polarities. |
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| AbstractList | The electroencephalogram EEG signal analysis is being strongly explored as a new potential tool for control, communication, and clinical diagnosis applications related to many neurological pathologies such as epilepsy, autism, Alzheimer. Analyzing EEG data is a very interesting approach to study cognitive processes. Thus, it can help researchers to understand the brain processes, doctor to establish an efficient medical diagnosis. To better understand the nature and the behavior of this signal which seems to be complex, nonlinear, non-stationary, and imbalanced, we propose to generate this signal using a predefined model of neurons with one and two dendrites by studying their activation functions. Then, we build a network of neurons with two dendrites correlated with the α and β frequencies which represent the most significant sub-bands in EEG signals to generate a signal quite close to EEG recordings. Through this paper, we aim to present a comprehensive study and an exploration of artificial EEG signal generation using only neurons with two dendrites that provided different activation and excitation functions. The presented result shows that the generated EEG signal with six neurons of two dendrites is quite similar to the real EEG recording for a healthy subjects. In addition, some artifacts like Alzheimer and Parkinson have been emulated and identified among the generated spikes. Finally we proved that we can eliminate some artifacts related to harmonics by applying activation function with opposite polarities. |
| ArticleNumber | 103699 |
| Author | Bouallegue, Ghaith Belwafi, Kais Djemal, Ridha |
| Author_xml | – sequence: 1 givenname: Ghaith surname: Bouallegue fullname: Bouallegue, Ghaith email: ghaithbouallegue@gmail.com organization: University of Sousse, ENISo of Sousse, Sousse, Tunisia – sequence: 2 givenname: Ridha surname: Djemal fullname: Djemal, Ridha organization: University of Sousse, Department of Electrical Engineering, High Institute of Applied Sciences and Technology of Sousse, Tunisia – sequence: 3 givenname: Kais surname: Belwafi fullname: Belwafi, Kais organization: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia |
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| Cites_doi | 10.1109/TNSRE.2019.2915621 10.1109/ACCESS.2019.2933814 10.3389/fnins.2020.00606 10.1155/2018/5174815 10.1016/j.compbiomed.2004.05.001 10.1016/j.chaos.2017.07.004 10.1063/1.522689 10.1038/s41572-019-0138-4 10.1016/j.bbe.2017.08.006 10.1016/j.neucom.2017.03.006 10.1016/j.epsr.2007.05.011 10.1016/j.jneumeth.2018.04.013 10.1109/IranianCEE.2019.8786636 10.1109/ICASSP.2018.8462243 10.1103/PhysRevD.18.4510 10.1109/ICASSP.2019.8683197 10.1038/s41598-018-24318-x 10.1016/j.eswa.2018.04.021 10.7555/JBR.34.20190026 10.1103/PhysRevLett.39.1379 10.1016/j.aml.2012.11.009 |
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| Keywords | Harmonic behavior Artificial neuron Electroencephalography (EEG) Artificial EEG modeling |
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article-title: Harmonic maps as models for physical theories publication-title: Phys Rev D doi: 10.1103/PhysRevD.18.4510 – ident: 10.1016/j.rinp.2020.103699_b0080 doi: 10.1109/ICASSP.2019.8683197 – ident: 10.1016/j.rinp.2020.103699_b0060 – volume: 8 start-page: 6828 year: 2018 ident: 10.1016/j.rinp.2020.103699_b0015 article-title: EEG analytics for early detection of autism spectrum disorder: a data-driven approach publication-title: Sci Rep doi: 10.1038/s41598-018-24318-x – ident: 10.1016/j.rinp.2020.103699_b0115 – volume: 107 start-page: 61 year: 2018 ident: 10.1016/j.rinp.2020.103699_b0055 article-title: An automated system for epilepsy detection using EEG brain signals based on deep learning approach publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.04.021 – volume: 34 start-page: 151 issue: 3 year: 2020 ident: 10.1016/j.rinp.2020.103699_b0020 article-title: EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms publication-title: J Biomed Res doi: 10.7555/JBR.34.20190026 – volume: 39 start-page: 1379 issue: 22 year: 1977 ident: 10.1016/j.rinp.2020.103699_b0130 article-title: Colliding impulsive gravitational-waves publication-title: Phys Rev Lett doi: 10.1103/PhysRevLett.39.1379 – ident: 10.1016/j.rinp.2020.103699_b0085 – volume: 9 start-page: 329 issue: 8 year: 2018 ident: 10.1016/j.rinp.2020.103699_b0040 article-title: EEG-based emotion recognition using 3D convolutional neural networks publication-title: Int J Adv Comput Sci Appl – volume: 26 start-page: 463 year: 2013 ident: 10.1016/j.rinp.2020.103699_b0095 article-title: The nonlinear Schrödinger harmonic oscillator problem with small odd or even disturbances publication-title: Appl Math Lett doi: 10.1016/j.aml.2012.11.009 |
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| Title | Artificial EEG signal generated by a network of neurons with one and two dendrites |
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