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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
› Author Affiliations
Further Information

Publication History

received: 05 October 2009

accepted: 12 May 2009

Publication Date:
17 January 2018 (online)

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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
 

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