An fMRI Study on the Spectral and Spatial Properties of Steady-State Visual Evoked Response

Steady-state visual evoked response (SSVER) has been widely used in cognitive research and engineering applications due to its high signal-to-noise ratio (SNR). Previous studies on SSVER explored its properties in the low and medium frequency bands (< 40 Hz), without any concern of the high frequency band (40-60 Hz). High-frequency SSVER has a considerable potential in BCI applications for its comfort and minimal visual fatigue caused. In this study, the spatial and spectral properties of SSVER were comprehensively investigated via functional MRI (fMRI). Two methods, GLM (general linear model) and spatial ICA (independent component analysis) were employed for the analysis of fMRI data. It was consistently found by the two methods that SSVER of the three frequency bands were located primarily in the visual cortex, with weaker activities occasionally detected in the parietal and frontal cortices. No differences in spatial distribution were found among the three frequency bands. Keywords-SSVER; SSVEP; fMRI; high frequency band; spatial property

[1]  S. Hillyard,et al.  Selective attention to stimulus location modulates the steady-state visual evoked potential. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[2]  M. D’Esposito,et al.  The variability of human BOLD hemodynamic responses , 1998, NeuroImage.

[3]  Manbir Singh,et al.  Correlation between BOLD‐fMRI and EEG signal changes in response to visual stimulus frequency in humans , 2003, Magnetic resonance in medicine.

[4]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[5]  G. Rees,et al.  Neuroimaging: Decoding mental states from brain activity in humans , 2006, Nature Reviews Neuroscience.

[6]  Christopher G. Thomas,et al.  Amplitude response and stimulus presentation frequency response of human primary visual cortex using BOLD EPI at 4 T , 1998, Magnetic resonance in medicine.

[7]  Yijun Wang,et al.  Lead selection for SSVEP-based brain-computer interface , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  M. Bianciardi,et al.  Single-epoch analysis of interleaved evoked potentials and fMRI responses during steady-state visual stimulation , 2009, Clinical Neurophysiology.

[9]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[10]  J. Rissanen A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .

[11]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[12]  M. D’Esposito,et al.  The Variability of Human, BOLD Hemodynamic Responses , 1998, NeuroImage.

[13]  Reto Meuli,et al.  fMRI responses in medial frontal cortex that depend on the temporal frequency of visual input , 2007, Experimental Brain Research.

[14]  Ramesh Srinivasan,et al.  Steady-State Visual Evoked Potentials: Distributed Local Sources and Wave-Like Dynamics Are Sensitive to Flicker Frequency , 2006, Brain Topography.

[15]  Gao Xiaorong,et al.  Brain-computer interface based on the high-frequency steady-state visual evoked potential , 2005, Proceedings. 2005 First International Conference on Neural Interface and Control, 2005..

[17]  G. Sperling,et al.  Attentional modulation of SSVEP power depends on the network tagged by the flicker frequency. , 2006, Cerebral cortex.

[18]  Matthias M. Müller,et al.  Magnetoencephalographic recording of steadystate visual evoked cortical activity , 2005, Brain Topography.

[19]  Laura Astolfi,et al.  Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle. , 2004, Magnetic resonance imaging.

[20]  P. Pietrini,et al.  Frequency Variation of a Pattern-Flash Visual Stimulus during PET Differentially Activates Brain from Striate through Frontal Cortex , 1997, NeuroImage.

[21]  Xiaorong Gao,et al.  Design and implementation of a brain-computer interface with high transfer rates , 2002, IEEE Transactions on Biomedical Engineering.

[22]  D. Regan,et al.  Human brain electrophysiology , 1989 .