PDF herunterladen
Methods Inf Med 2010; 49(05): 496-500
DOI: 10.3414/ME09-02-0031
Special Topic – Original Articles
Schattauer GmbH

Quantifying Changes in EEG Complexity Induced by Photic Stimulation

S. Erla
1   Biophysics and Biosignals Lab., Dept. of Physics (Biotech), University of Trento, Trento, Italy
2   MEG Lab., Laboratory of Functional Neuroimaging (CIMeC), University of Trento, Trento, Italy
,
L. Faes
1   Biophysics and Biosignals Lab., Dept. of Physics (Biotech), University of Trento, Trento, Italy
,
G. Nollo
1   Biophysics and Biosignals Lab., Dept. of Physics (Biotech), University of Trento, Trento, Italy
› Institutsangaben
Weitere Informationen

Publikationsverlauf

received: 05. Oktober 2009

accepted: 12. Mai 2009

Publikationsdatum:
17. Januar 2018 (online)

    Lizenzen und Reprints

Summary

Objectives: This study aims to characterize EEG complexity, measured as the prediction error resulting from nonlinear prediction, in healthy humans during photic stimulation.

Methods: EEGs were recorded from 15 subjects with eyes closed (EC) and eyes open (EO), during the baseline condition and during stroboscopic photic stimulation (PS) at 5, 10, and 15 Hz. The mean squared prediction error (MSPE) resulting from nearest neighbor local linear prediction was taken as complexity index. Complexity maps were generated interpolating the MSPE index over a schematic scalp representation.

Results: Statistical analysis revealed that: i) EEG shows good predictability in all conditions and seems to be well explained by a linear stochastic process; ii) the complexity is lower with EC than with EO and increases significantly during PS, to a lesser extent during 10 Hz stimulation; iii) significant differences of EEG complexity are detectable between anterior-central and posterior scalp regions.

Conclusions: Changes in EEG complexity during PS can be successfully assessed using nonlinear prediction. The observed modifications in the patterns of complexity seem to reflect neurophysiological behaviors and suggest future applicability of the method in clinical settings.

Keywords

EEG - local linear prediction - predictability maps - visual stimulation
 

  • References

  • 1 Coull BM, Pedley TA. Intermittent photic stimulation Clinical usefulness of non-conclusive responses. Electroencephalogr Clin Neurophysiol 1978; 44: 353-363.
    CrossrefPubMedGoogle Scholar
  • 2 Ricci S, Vigevano F. Occipital seizures provoked by intermittent light stimulation – ictal and interictal findings. J Clin Neorophysiol 1993; 10 (02) 197-209.
    PubMedGoogle Scholar
  • 3 Widdig W, Pleger B, Rommel O, Malin J-P, Tegenthoff M. Repetitive visual stimulation: A neuropsychological approach to the treatment of cortical blindness. NeuroRehabilitation 2003; 18: 227-237.
    PubMedGoogle Scholar
  • 4 Frederick JA, Timmermann DAL, Russel HL, Lubar JF. EEG coherence effects of audio-visual stimulation (APS) at dominant and twice dominant alpha frequency. J Neurotherapy 2004; 8: 25-42.
    PubMedGoogle Scholar
  • 5 Kirschfeld K. The physical basis of alpha waves in the electroencephalogram and the origin of the Berger effect. Biol Cybern 2005; 92: 177-185.
    CrossrefPubMedGoogle Scholar
  • 6 Stam CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 2005; 116: 2266-2301.
    CrossrefPubMedGoogle Scholar
  • 7 Kobayashi T, Madokoro S, Wada Y, Misaki K, Nakagawa H. Human sleep EEG analysis using the correlation dimension. Clin Electroencephalogr 2001; 32 (03) 112-118.
    CrossrefPubMedGoogle Scholar
  • 8 Rapp PE, Bashore TR, Martinerie JM, Albano AM, Zimmerman ID, Mees AI. Dynamics of brain electrical activity. Brain Topogr 1989; 2: 99-118.
    CrossrefPubMedGoogle Scholar
  • 9 Blinowska KJ, Malinowski M. Non-linear and linear forecasting of the EEG time series. Biol Cybern 1991; 66: 159-165.
    CrossrefPubMedGoogle Scholar
  • 10 Brandt ME, Ademoglu A, Pritchard WS. Nonlinear prediction and complexity of alpha EEG activity. Int J Bifurcat Chaos 2000; 10: 123-133.
    CrossrefPubMedGoogle Scholar
  • 11 Farmer JD, Sidorowich JJ. Predicting Chaotic time series. Phys Rev Lett 1987; 59: 845-848.
    CrossrefPubMedGoogle Scholar
  • 12 Jung TP, Humphries C, Lee TW, Makeig S, McKeown MJ, Iragui V, Sejnowski TJ. Removing electroencephalographic artifacts by blind source separation. Psychophysiology 2000; 37: 163-178.
    CrossrefPubMedGoogle Scholar
  • 13 Takens F. Detecting strange attractors in turbulence. Lecture Notes in Mathematics 1981; 898: 366-381.
    PubMedGoogle Scholar
  • 14 Fraser AM, Swinney HL. Independent coordinates for strange attractors from mutual information. Phys Rev A 1986; 33: 1134-1140.
    CrossrefPubMedGoogle Scholar
  • 15 Kennel MB, Brown R, Abarbanel HDI. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A Gen Phys 1992; 45: 3403-3411.
    CrossrefPubMedGoogle Scholar
  • 16 Holm SA. Simple sequentially rejective multiple test procedure. Scand J Statistics 1979; 6: 65-70.
    PubMedGoogle Scholar
  • 17 Kay SM. Modern Spectral Estimation, theory and applications. Prentice Hall; 1988
    Google Scholar
  • 18 Teplan M, Krakovká A, Štolc S. EEG responses to long-term audio-visual stimulation. Int J Psycho-physiol 2006; 59: 81-90.
    CrossrefPubMedGoogle Scholar
  • 19 Babiloni C, Binetti G, Cassarino A, Dal Forno G, Del Percio C, Ferreri F, Ferri R, Frisoni G, Galderisi S, Hirata K, Lanuzza B, Miniussi C, Mucci A, Nobili F, Rodriguez G, Luca RG, Rossini PM. Sources of cortical rhythms in adults during physiological aging: a multicentric EEG study. Hum Brain Mapp 2006; 27: 162-172.
    CrossrefPubMedGoogle Scholar
  • 20 Wada Y, Nanbu T, Kikuchi M, Koshino Y, Hashimoto T, Yamaguchi N. Abnormal functional connectivity in Alzheimer’s disease: Intrahemispheric EEG coherence during rest and photic stimulation. Eur Arch Psychiatry Clin Neurosci 1998; 248: 203-208.
    CrossrefPubMedGoogle Scholar
  • 21 Ahmed SN, Unterman J, McCloskey B, Sinclair DB. Clinical significance of photic stimulation during routine EEGs of adult patients. Am J Electroneurodiagnostic Technol 2006; 46: 356-362.
    PubMedGoogle Scholar