Objective. Flickering visual stimulation is known to evoke rhythmic oscillations in the electroencephalographic (EEG) activity, called steady-state visually evoked potentials (SSVEP). The presence of harmonic components in the EEG signals during SSVEP suggests the non-linearity of the visual-system response to rhythmic stimulation, but the nature of this behavior has not been deeply understood. The aim of this study is the quantitative evaluation and characterization of this non-linear phenomenon and its interference with the physiological alpha rhythm by means of spectral and higher order spectral analysis. Approach. EEG signals were acquired in a group of 12 healthy subjects during a pattern-reversal stimulation protocol at three different driving frequencies (7.5 Hz, 15 Hz and 24 Hz). Spectral power values were estimated, after Laplacian spatial filtering, to quantitatively evaluate the changes in the power of the individual alpha and stimulation frequencies related harmonic components. Bicoherence measure were employed to assess the presence of quadratic phase coupling (QPC) at each channel location. Main results. Our analysis confirmed a strong non-linear response to the rhythmic stimulus principally over the parieto-occipital channel locations and a simultaneous significant alpha power suppression during 7.5 Hz and 15 Hz stimulation. A prominent sub-harmonic component characterized the resonance behavior of the 24 Hz stimulation. Significance. The findings presented suggest that bicoherence is a useful tool for the identification of QPC interactions between stimulus-related frequency components within the same signal and the characterization of the non-linearity of SSVEP-induced harmonics generation. In addition, the applied methodology demonstrates the presence of coupled EEG rhythms (harmonics of the main oscillation) both in resting condition and during stimulation, with different characteristics in the distinct brain areas.
- photic driving
ASJC Scopus subject areas
- Biomedical Engineering
- Cellular and Molecular Neuroscience