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Year 2020, Volume: 5 Issue: 2, 94 - 98, 31.12.2020

Ömer Akgün

Abstract

References

  • [1] https://www.doktorfizik.com/sinir-sistemi/norobilim/beyinde-hareket-merkezi-neresidir/, 12 July 2019
  • [2] A.M. Badakva, N.V. Miller, L.N. Zobova, “New Concepts on the Implementation of Motor and Cognitive Functions in the Brain: Facts and Hypotheses”, Human Physiology, Vol.46, No.3, 2020, pp.343-350.
  • [3] F. Zhou, Y. Liuye, Q. Zhiyu, “Research on Multi-parameter Visualization Technology of Brain Function Based on EEG”, 2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE), 1-4 Nov, 2019.
  • [4] N.N. Dygalo, “Investigation of Brain Functions Using Genetically Encoded Tools”, Neuroscience and Behavioral Physiology, Vol.50, No.8, 2020, pp.1051-1056.
  • [5] E.N. Vanlı Yavuz, N. Bebek, “Place of Electroencephalography (EEG) in the Diagnosis and Treatment of Epilepsy”, Klinik Gelişim, pp. 35-38.
  • [6] M. Duraıraj, R. Jansı Ranı, “Mining and Extracting Emotional Information from Human-Based EEG: An Overview”, Productivity, Vol. 59, No. 4, 2019, pp.343-351.
  • [7] S. Sanei, J.A. Chambers, EEG Signal Processing, Wiley-Interscience; 1st edition, 2013.
  • [8] S. Yan, L.Y. Zhang, EEG Signal Analysis and Classification, Springer Nature Switzerland, 2020. [9] B. Baykan, E. Altındağ, A.D. Elmalı, Elektroensefalografi, http://www.itfnoroloji.org/semi2/eeg.htm
  • [10] 10. www.researchgate.net/publication/321094865
  • [11] A. Riyadi, Munawar, T. Prakoso, ; F.O. Whaillan, ; D.W. Marcelinus, A. Hidayatno, “Classification of EEG-based Brain Waves for Motor Imagery using Support Vector Machine”, 2019 International Conference on Electrical Engineering and Computer Science (ICECOS), 422-425 Oct, 2019.
  • [12] O.S. Sushkova, A.A. Morozov, A.V. Gabova, “A method of analysis of EEG wave trains in early stages of Parkinson's disease”, 2016 International Conference on Bioinformatics and Systems Biology (BSB),
  • [13] A. Quintero-Rincón, V. Muro, C. D’Giano, J. Prendes, H. Batatia, “Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals”, Computers, Vol 9, No 85, pp. 85.
  • [14] https://github.com/meagmohit/EEG-Datasets
  • [15] www.baskent.edu.tr › ~tbayrak › LAB7
  • [16] Francis Castanié (Editor), Digital Spectral Analysis: Parametric, Non-Parametric and Advanced Methods, Wiley, 2011.
  • [17] M. Tosun, M. Erginli, O. Kasım, B. Uğraş, S. Tanrıverdi, T. Kavak, “By Using EEG data, Classification of the Physical Hand Movements and the Imagination of these movements via Artificial Neural Networks”, Sakarya Unıversıty Journal Of Computer And Informatıon Scıences, Vol.1, No.2, 2018.
  • [18] K.A. Budunova, V.F. Kravchenko, D. Churikov, “Application of the Family of Kravchenko-Rvachev Atomic Weight Functions (Windows) in Welch Method EEG Power Spectral Density Estimation”, 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring). 2019, pp.500-506.

EFFECTS OF REAL AND IMAGINARY HAND MOVEMENTS ON EEG

Year 2020, Volume: 5 Issue: 2, 94 - 98, 31.12.2020

Ömer Akgün

Abstract

Our brain is one of the most complex structures in the known universe, and no matter how we want to make it simple, the way of work of it is quite complicated. In this study, the brain's structure and function that enable our movements were briefly discussed. Electroencephalography (EEG) is a non-invasive method of examination in which spontaneous electrical activity of the brain is recorded through electrodes. This examination reflects the current functional state of the brain rather than its structural properties.This study is based on an analysis of EEG signals obtained through a subject raising his left and right arm forward and then imagining him raising his left and right arm forward. First, the change of signals by the time was obtained, then the amplitude spectra of EEG signals were reached by applying the Fast Fourier Transform. Finally, the power spectrum analysis of signals was performed using the Welch method.

Keywords

Hand movements EEG Histogram FFT Welch method

References

  • [1] https://www.doktorfizik.com/sinir-sistemi/norobilim/beyinde-hareket-merkezi-neresidir/, 12 July 2019
  • [2] A.M. Badakva, N.V. Miller, L.N. Zobova, “New Concepts on the Implementation of Motor and Cognitive Functions in the Brain: Facts and Hypotheses”, Human Physiology, Vol.46, No.3, 2020, pp.343-350.
  • [3] F. Zhou, Y. Liuye, Q. Zhiyu, “Research on Multi-parameter Visualization Technology of Brain Function Based on EEG”, 2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE), 1-4 Nov, 2019.
  • [4] N.N. Dygalo, “Investigation of Brain Functions Using Genetically Encoded Tools”, Neuroscience and Behavioral Physiology, Vol.50, No.8, 2020, pp.1051-1056.
  • [5] E.N. Vanlı Yavuz, N. Bebek, “Place of Electroencephalography (EEG) in the Diagnosis and Treatment of Epilepsy”, Klinik Gelişim, pp. 35-38.
  • [6] M. Duraıraj, R. Jansı Ranı, “Mining and Extracting Emotional Information from Human-Based EEG: An Overview”, Productivity, Vol. 59, No. 4, 2019, pp.343-351.
  • [7] S. Sanei, J.A. Chambers, EEG Signal Processing, Wiley-Interscience; 1st edition, 2013.
  • [8] S. Yan, L.Y. Zhang, EEG Signal Analysis and Classification, Springer Nature Switzerland, 2020. [9] B. Baykan, E. Altındağ, A.D. Elmalı, Elektroensefalografi, http://www.itfnoroloji.org/semi2/eeg.htm
  • [10] 10. www.researchgate.net/publication/321094865
  • [11] A. Riyadi, Munawar, T. Prakoso, ; F.O. Whaillan, ; D.W. Marcelinus, A. Hidayatno, “Classification of EEG-based Brain Waves for Motor Imagery using Support Vector Machine”, 2019 International Conference on Electrical Engineering and Computer Science (ICECOS), 422-425 Oct, 2019.
  • [12] O.S. Sushkova, A.A. Morozov, A.V. Gabova, “A method of analysis of EEG wave trains in early stages of Parkinson's disease”, 2016 International Conference on Bioinformatics and Systems Biology (BSB),
  • [13] A. Quintero-Rincón, V. Muro, C. D’Giano, J. Prendes, H. Batatia, “Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals”, Computers, Vol 9, No 85, pp. 85.
  • [14] https://github.com/meagmohit/EEG-Datasets
  • [15] www.baskent.edu.tr › ~tbayrak › LAB7
  • [16] Francis Castanié (Editor), Digital Spectral Analysis: Parametric, Non-Parametric and Advanced Methods, Wiley, 2011.
  • [17] M. Tosun, M. Erginli, O. Kasım, B. Uğraş, S. Tanrıverdi, T. Kavak, “By Using EEG data, Classification of the Physical Hand Movements and the Imagination of these movements via Artificial Neural Networks”, Sakarya Unıversıty Journal Of Computer And Informatıon Scıences, Vol.1, No.2, 2018.
  • [18] K.A. Budunova, V.F. Kravchenko, D. Churikov, “Application of the Family of Kravchenko-Rvachev Atomic Weight Functions (Windows) in Welch Method EEG Power Spectral Density Estimation”, 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring). 2019, pp.500-506.
There are 17 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Articles
Authors

Ömer Akgün 0000-0003-3486-2197

Publication Date December 31, 2020
Published in Issue Year 2020 Volume: 5 Issue: 2

Cite

APA Akgün, Ö. (2020). EFFECTS OF REAL AND IMAGINARY HAND MOVEMENTS ON EEG. The Journal of Cognitive Systems, 5(2), 94-98.
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