Author: Halbleib A, Gratkowski M, Schwab K, Ligges C, Witte H, Haueisen J.
*Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany †Institute of Medical Statistics, Computer Sciences and Documentation ‡Departments of Child and Adolescent Psychiatry §Neurology, Biomagnetic Center, Friedrich Schiller University Jena, Jena, Germany ‖Department of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.
Conference/Journal: J Clin Neurophysiol.
Date published: 2012 Feb
Other: Volume ID: 29 , Issue ID: 1 , Pages: 33-41 , Word Count: 221
A coupled system of nonlinear neural oscillators with an individual resonance frequency is assumed to form the neuronal substrate for the photic driving phenomenon. The aim was to investigate the spatiotemporal stability of these oscillators and quantify the spatiotemporal process of engagement and disengagement of the neuronal oscillators in both multitrial and single-trial data.
White light-emitting diode flicker stimulation was used at 15 frequencies, which were set relative to the individual α frequency of each of the 10 healthy participants. Simultaneously, the electroencephalogram (EEG) and the magnetoencephalogram (MEG) were recorded. Subsequently, spatiotemporal matching pursuit (MP) algorithms were used to analyze the EEG and MEG topographies.
Intraindividually similar topographies were found at stimulation frequencies close to (1) the individual α frequency and (2) half the individual α frequency in the multitrial and the single-trial cases. In both stimulation frequency ranges, the authors observed stable topographies 5 to 10 stimuli after the beginning of the stimulation and lasting three nonexisting periods after the end of the stimulation. This was interpreted as the engaging/disengaging effect of the observed oscillations, because especially the frequency parameter adopted before and after stable topographies were observed. Topographic entrainment was slightly more pronounced in MEG as compared with that in EEG.
The results support the hypothesis of nonlinear information processing in human visual system, which can be described by nonlinear neural oscillators.