Interaction between steady-state visually evoked potentials at nearby flicker frequencies

Abstract

Steady-state visually evoked potential (SSVep) studies routinely employ simultaneous presentation of two temporally modulated stimuli, with SSVEP amplitude modulations serving to index top-down cognitive processes. However, the nature of SSVEP amplitude modulations as a function of competing temporal frequency (TF) has not been systematically studied, especially in relation to the normalization framework which has been extensively used to explain visual responses to multiple stimuli. We recorded spikes and local field potential (LFP) from the primary visual cortex (V1) as well as EEG from two awake macaque monkeys while they passively fixated plaid stimuli with components counterphasing at different TFs. We observed asymmetric SSVEP response suppression by competing TFs (greater suppression for lower TFs), which further depended on the relative orientations of plaid components. A tuned normalization model, adapted to SSVEP responses, provided a good account of the suppression. Our results provide new insights into processing of temporally modulated visual stimuli.

The SSVEP tracks attention, not consciousness, during perceptual filling-in

“…Insofar as the strength of target SSVEP signals when measured with EEG may reflect the strength of target-specific spike activity in the sensory cortex, our finding does challenge such theories that propose response strength is positively correlated with the vividness of conscious experience. Currently, however, the exact mechanisms of SSVEP generation are not well understood ( Kawashima et al, 2020 ; Kim et al, 2017 ; Norcia et al, 2015 ; Notbohm et al, 2016 ; Rager and Singer, 1998 ; Salelkar and Ray, 2020 ), and future studies will be necessary to clarify their input.…”

“…For example, first-order theories of conscious perception (Mehta & Mashour, 2013;Zeki, 2007) propose that the strength of activity within contentspecific visual processing areas determines the vividness of that visual experience. Currently, however, the exact mechanisms of SSVEP generation are not well understood (Kawashima, Li, Chen, Vickery, & Morley, 2020;Kim et al, 2017;Norcia et al, 2015;Notbohm, Kurths, & Herrmann, 2016;Rager & Singer, 1998;Salelkar & Ray, 2020), and future studies will be necessary to clarify their input.…”

Stimulus-induced gamma rhythms are weaker in human elderly with mild cognitive impairment and Alzheimer’s disease

“…We chose 32 Hz SSVEP (induced by gratings of temporal frequency of 16 cycles per second or cps) for two reasons. First, in a separate set of experiments, we had recorded spikes and local field potentials (LFP) in the primary visual cortex of awake monkeys while presenting counterphasing gratings at varying temporal frequencies and found that the SSVEP gain was highest for gratings with temporal frequencies of 12 to 16 cps (Salelkar and Ray (2020)).…”

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach

“…where the transfer rate B can be translated into bits/minute, once it is multiplied by the frequency of decisions per minute T, as permitted in the BCI system shown in expression (21).…”

“…On the other hand, the amplitude and phase of SSVEP responses are highly sensitive to stimulus parameters such as repetition rate, color, luminance or modulation depth, and spatial frequency [ 19 , 20 ]. However, the SSVEP paradigm stands out for its minimal training capacity; robustness, high signal-to-noise ratio (SNR); and high information transfer rate (ITR) [ 21 , 22 ]. Furthermore, for the analysis and design of BCI systems, it is necessary to consider three fundamental aspects: the short processing time, the high information transfer rate (ITR), and the fact that these should be as noninvasive as possible [ 23 , 24 ].…”