SavedAdd your journal

Journals / Marmara Medical Journal / 2006 / Volume: 19 Issue: 1

-

EEG TEMELLİ OLAY-İLİŞKİ BEYİN DALGALARININ İSTATİSTİKSEL ZAMAN SERİLERİ İLE MODELLENMESİ

Ömer Erzengin
Vildan Sümbüloğlu
Sirel Karakaş
Pages
—
Publication Date
2015-06-26
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗

Abstract

-

Özet

Amaç: Çalışmanın amacı beyinin olay ilişki aktivesinin alfa osilasyonlarının Otoregresif Bütünleşik Hareketli Ortalama - ARIMA – modeli ile açıklamaktır. Yöntem: Çalışan örneklem 17 (11 erkek, 6 kadın) sağ elini kullanan, sağlıklı gönüllülerden oluşmuştur. Olay ilişkili aktivite, atlanmış uyarıcı paradigması altında kaydedilmiştir. Alfa tepkisi sayısal filtreyle elde edilmiştir. Her katılımcının alfa tepksi ayrı ayrı analiz edilmiştir. Bulgular: Yapılan çalışma göstermektedir ki her bir katılımcının atlanmış uyarıcı paradigması için alfa tepkisi, ARIMA (0,2,1) tipindeki zaman serisiyle en iyi modellenebilmektedir. Sonuç: Yapılan çalışma atlanmış uyarıcı paradigması altında elde edilmiş, bir katılımcının alfa tepkilerinin en iyi ARIMA (0, 2, 1) zaman serisiyle modellendiğini göstermiştir.

🔖

Cite This Article

Erzengin, Ö., Sümbüloğlu, V., Karakaş, S. (2015). -. Marmara Medical Journal, Volume: 19 Issue: 1. https://ajindex.com/articles/1287144

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (22)

  1. Karakaş S, Erzengin ÖU, Başar E. A new strategy involving multiple cognitive paradigms demonstrates that ERP components are determined by the superposition of oscillatory responses. Clin Neurophysiol 2000; 111: 1719-1732
  2. Başar E. EEG-Brain Dynamics: Relation between EEG and Brain Evoked-Potentials. Amsterdam: Elsevier Publication,1980.
  3. Brandt ME, Jansen BH. The relationship between prestimulus alpha amplitude and visual evoked potential amplitude. Int J Neurosci 1991; 61: 261-8.
  4. Jervis BW, Nichols MJ, Johnson TE, Allen, Hudson NR. A fundamental investigation of the composition of auditory evoked potentials. IEEE Trans Biomed Eng 1983; 30: 43-49.
  5. Kolev V, Yordanova J. Analysis of phase-locking is informative for studying event-related EEG activity. Biol Cybern 1997; 96: 229-235.
  6. Parvin C, Torres F, Johnson E. Synchronization of single evoked response components: Estimation and interrelation of reproducibility measures. In Rhythmic EEG Activities and Cortical Functioning. Amsterdam: Elsevier, 1980.
  7. Röschke J, Mann K, Riemann D, Frank C, Fell J. Sequential analysis of the brain’s transfer properties during consecutive REM periods. Electroencephalogr Clin Neurophysiol 1995; 96: 390-397.
  8. Solodovnikov VV. Introduction to the Statistical Dynamics of Automatic Control Systems. New York: Dover Publications 1960.
  9. Başar E. Brain Function and Oscillations I: Brain Oscillations. Principles and Approaches Heidelberg: Springer-Verlag 1998.
  10. Başar E. Brain Function and Oscillations II: Integrative Brain Function. Neurophysiology and Cognitive Processes. Heidelberg: Springer-Verlag 1999.
  11. Cohen L. Time-frequency distributions: A review. Proc IEEE 1989; 77 (7): 941-81.
  12. Demiralp T, Ademoğlu A, Schürmann M, Başar E. Wavelet analysis of brain waves. In: Başar E, editor. Brain Function and Oscillations: I. Brain Oscillations. Principles and Approaches. Heidelberg: Springer-Verlag 1998.
  13. Demiralp T, Ademoğlu A, Schürmann M, Başar-Eroğlu C, Başar E. Detection of P300 in single trials by the wavelet transform (WT). Brain Lang 1999; 66: 108-128.
  14. Özdemir AK., Karakaş S, Çakmak ED, Tüfekçi Dİ., Arıkan O. Time-frequency component analyser and its application to brain oscillatory activity. Journal of Neuroscience Methods 2005; 145: 107-125.
  15. Rosso OA, Blanco S, Yordanova J, Kolev V, Figliola A, Schürmann M, Başar E. Wavelet entropy: A new tool for analysis of short-duration brain electrical signals. J Neurosci Methods 2001; 105: 65-75.
  16. Samar VJ, Bobardikar A, Rao R, Swartz K. Wavelet analysis of neuroelectric waveforms: A conceptual tutorial. Brain and Lang 1999; 66: 7-60.
  17. Tağluk, M.E., Çakmak, E.D., Karakaş, S. Analysis of time-varying energy of brain responses to an oddball paradigm using short-term smoothed Wigner-Ville distribution. Journal of Neuroscience Methods 2005; 143: 197-208.
  18. Yordonova J, Kolev V, Rosso OA, et al. Wavelet entropy analysis of event-related potentials indicates modality of independent theta dominance. J Neurosci Methods 2002; 117: 99-109.
  19. Farwell LA, Martinerie JM, Bashore TR, Rapp PE, Goddard PH. Optimal digital filters for long-latency components of the event-related brain potential. Psychophysiology 1993; 30: 306-315.
  20. Cook III. EW, Miller GA. Digital filtering: Background and tutorial for psychophysiologists. Psychophysiology 1992; 29: 350-367.
  21. Box GEP, Jenkins GM, Reinsel GC. Time Series Analysis, Forecasting and Control, 3rd ed. NJ: Prentice Hall 1994.
  22. Demiralp T, Başar E. Theta rhytmicities following expected visual and auditory targets. Int J Psychophysiol 1992; 13: 147-160