Higher order spectral analysis of scalp EEG activity reveals non-linear behavior during rhythmic visual stimulation

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. .

[1]  T. Gasser,et al.  Statistical methods for investigating phase relations in stationary stochastic processes , 1971 .

[2]  E. Powers,et al.  Digital Bispectral Analysis and Its Applications to Nonlinear Wave Interactions , 1979, IEEE Transactions on Plasma Science.

[3]  M.R. Raghuveer,et al.  Bispectrum estimation: A digital signal processing framework , 1987, Proceedings of the IEEE.

[4]  F. Perrin,et al.  Spherical splines for scalp potential and current density mapping. , 1989, Electroencephalography and clinical neurophysiology.

[5]  J. Bronzino,et al.  Nonlinear analysis of the hippocampal subfields of CA1 and the dentate gyrus , 1993, IEEE Transactions on Biomedical Engineering.

[6]  C. L. Nikias,et al.  Signal processing with higher-order spectra , 1993, IEEE Signal Processing Magazine.

[7]  Y. Wada,et al.  Interhemispheric EEG coherence during photic stimulation: sex differences in normal young adults. , 1996, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[8]  M. Stecker,et al.  Bispectral analysis of visual interactions in humans. , 1996, Electroencephalography and clinical neurophysiology.

[9]  P. Husar,et al.  Bispectrum analysis of visually evoked potentials , 1997, IEEE Engineering in Medicine and Biology Magazine.

[10]  T. Bullock,et al.  Bicoherence of intracranial EEG in sleep, wakefulness and seizures. , 1997, Electroencephalography and clinical neurophysiology.

[11]  Shen Minfen,et al.  Parametric bispectral estimation of EEG signals in different functional states of brain , 2000 .

[12]  Mingui Sun,et al.  Characterization of sleep spindles using higher order statistics and spectra , 2000, IEEE Transactions on Biomedical Engineering.

[13]  H. Witte,et al.  Quantification of transient quadratic phase couplings within EEG burst patterns in sedated patients during electroencephalic burst-suppression period , 2000, Journal of Physiology-Paris.

[14]  M. Takashina,et al.  Practical Issues in Bispectral Analysis of Electroencephalographic Signals , 2001, Anesthesia and analgesia.

[15]  V. Lazarev,et al.  Photic driving in the electroencephalogram of children and adolescents: harmonic structure and relation to the resting state. , 2001, Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas.

[16]  C. Herrmann.  Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena , 2001, Experimental Brain Research.

[17]  S. Cerutti,et al.  Spectral and bispectral analysis of the EEG rhythms in basal conditions and during photic stimulation , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Luca Faes,et al.  Surrogate data analysis for assessing the significance of the coherence function , 2004, IEEE Transactions on Biomedical Engineering.

[19]  Arjan Hillebrand,et al.  The temporal frequency tuning of human visual cortex investigated using synthetic aperture magnetometry , 2004, NeuroImage.

[20]  Daniele Marinazzo,et al.  Steady-state visual evoked potentials and phase synchronization in migraine patients. , 2004, Physical review letters.

[21]  C. Jeleazcov,et al.  Does the EEG During Isoflurane/Alfentanil Anesthesia Differ from Linear Random Data? , 2002, Journal of Clinical Monitoring and Computing.

[22]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[23]  Nassib G. Chamoun,et al.  An introduction to bispectral analysis for the electroencephalogram , 1994, Journal of Clinical Monitoring.

[24]  Reinhold Scherer,et al.  Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components , 2005, Journal of neural engineering.

[25]  E. Basar.  The theory of the whole-brain-work. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[26]  L. Carmant,et al.  Interaction between the flash evoked SSVEPs and the spontaneous EEG activity in children and adults , 2006, Clinical Neurophysiology.

[27]  A. Priori,et al.  Dopamine‐dependent non‐linear correlation between subthalamic rhythms in Parkinson's disease , 2006, The Journal of physiology.

[28]  Michel Le Van Quyen,et al.  Analysis of dynamic brain oscillations: methodological advances , 2007, Trends in Neurosciences.

[29]  S. Cerutti,et al.  Interaction Between Rhythms in the Human Basal Ganglia: Application of Bispectral Analysis to Local Field Potentials , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[30]  Yoichi Saito,et al.  Quantitative evaluation of photic driving response for computer-aided diagnosis , 2008, Journal of neural engineering.

[31]  David J. Thomson,et al.  Canonical bicoherence analysis of dynamic EEG data , 2010, Journal of Computational Neuroscience.

[32]  G. Avanzini,et al.  Photosensitive epilepsy: Spectral and coherence analyses of EEG using 14Hz intermittent photic stimulation , 2010, Clinical Neurophysiology.

[33]  Ronald M. Aarts,et al.  A Survey of Stimulation Methods Used in SSVEP-Based BCIs , 2010, Comput. Intell. Neurosci..

[34]  A. Cichocki,et al.  Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives , 2010, Progress in Neurobiology.

[35]  U. Rajendra Acharya,et al.  Analysis and Automatic Identification of Sleep Stages Using Higher Order Spectra , 2010, Int. J. Neural Syst..

[36]  I. Tetko,et al.  Cross-frequency coupling in mesiotemporal EEG recordings of epileptic patients , 2010, Journal of Physiology-Paris.

[37]  Xiaorong Gao,et al.  Functional connectivity analysis of steady-state visual evoked potentials , 2011, Neuroscience Letters.

[38]  G. Avanzini,et al.  Enhanced frontocentral EEG connectivity in photosensitive generalized epilepsies: A partial directed coherence study , 2012, Epilepsia.

[39]  T. Wilson,et al.  Presence of strong harmonics during visual entrainment: A magnetoencephalography study , 2012, Biological Psychology.

[40]  P. Robinson,et al.  Experimental observation of a theoretically predicted nonlinear sleep spindle harmonic in human EEG , 2014, Clinical Neurophysiology.

[41]  Kazuko Hayashi,et al.  Simultaneous bicoherence analysis of occipital and frontal electroencephalograms in awake and anesthetized subjects , 2014, Clinical Neurophysiology.

[42]  Justin M. Ales,et al.  The steady-state visual evoked potential in vision research: A review. , 2015, Journal of vision.

[43]  D. Yao,et al.  The graph theoretical analysis of the SSVEP harmonic response networks , 2015, Cognitive Neurodynamics.

[44]  Basabdatta Sen Bhattacharya,et al.  Nonlinear Origin of SSVEP Spectra—A Combined Experimental and Modeling Study , 2016, Front. Comput. Neurosci..

[45]  S. Billings,et al.  Nonlinear interactions in the thalamocortical loop in essential tremor: A model-based frequency domain analysis , 2016, Neuroscience.

[46]  S. Cerutti,et al.  Investigation of the electrophysiological correlates of negative BOLD response during intermittent photic stimulation: An EEG‐fMRI study , 2016, Human brain mapping.

[47]  Michael X. Cohen,et al.  Individual Alpha Peak Frequency Predicts 10 Hz Flicker Effects on Selective Attention , 2017, The Journal of Neuroscience.

[48]  Christopher K. Kovach,et al.  The bispectrum and its relationship to phase-amplitude coupling , 2017, NeuroImage.

[49]  Yangsong Zhang,et al.  Periodic Visual Stimulation Induces Resting-State Brain Network Reconfiguration , 2018, Front. Comput. Neurosci..

[50]  Wojciech Samek,et al.  Localizing bicoherence from EEG and MEG , 2018, NeuroImage.