Quantifying Changes in EEG Complexity Induced by Photic Stimulation

S. Erla; L. Faes; G. Nollo

doi:10.3414/ME09-02-0031

Methods of Information in Medicine, Table of Contents

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

Authors

S. Erla

¹Biophysics and Biosignals Lab., Dept. of Physics (Biotech), University of Trento, Trento, Italy

²MEG Lab., Laboratory of Functional Neuroimaging (CIMeC), University of Trento, Trento, Italy
L. Faes

¹Biophysics and Biosignals Lab., Dept. of Physics (Biotech), University of Trento, Trento, Italy
G. Nollo

¹Biophysics and Biosignals Lab., Dept. of Physics (Biotech), University of Trento, Trento, Italy

Abstract

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

Full Text

References

References
1 Coull BM, Pedley TA. Intermittent photic stimulation Clinical usefulness of non-conclusive responses. Electroencephalogr Clin Neurophysiol 1978; 44: 353-363.
2 Ricci S, Vigevano F. Occipital seizures provoked by intermittent light stimulation – ictal and interictal findings. J Clin Neorophysiol 1993; 10 (02) 197-209.
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.
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.
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.
6 Stam CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 2005; 116: 2266-2301.
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.
8 Rapp PE, Bashore TR, Martinerie JM, Albano AM, Zimmerman ID, Mees AI. Dynamics of brain electrical activity. Brain Topogr 1989; 2: 99-118.
9 Blinowska KJ, Malinowski M. Non-linear and linear forecasting of the EEG time series. Biol Cybern 1991; 66: 159-165.
10 Brandt ME, Ademoglu A, Pritchard WS. Nonlinear prediction and complexity of alpha EEG activity. Int J Bifurcat Chaos 2000; 10: 123-133.
11 Farmer JD, Sidorowich JJ. Predicting Chaotic time series. Phys Rev Lett 1987; 59: 845-848.
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.
13 Takens F. Detecting strange attractors in turbulence. Lecture Notes in Mathematics 1981; 898: 366-381.
14 Fraser AM, Swinney HL. Independent coordinates for strange attractors from mutual information. Phys Rev A 1986; 33: 1134-1140.
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.
16 Holm SA. Simple sequentially rejective multiple test procedure. Scand J Statistics 1979; 6: 65-70.
17 Kay SM. Modern Spectral Estimation, theory and applications. Prentice Hall; 1988
18 Teplan M, Krakovká A, Štolc S. EEG responses to long-term audio-visual stimulation. Int J Psycho-physiol 2006; 59: 81-90.
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.
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.
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.