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Anthony M. Norcia, L. Gregory Appelbaum, Justin M. Ales, Benoit R. Cottereau, Bruno Rossion; The steady-state visual evoked potential in vision research: A review. Journal of Vision ;15 6 Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception.
From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.
The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science. Purchase this article with an account. Jump To Introduction Responses to single periodic visual inputs Multiple periodic visual inputs Multi-input interactions as an objective measurement of system nonlinearities and neural convergence Current status and future directions Acknowledgments References Appendices.
Review May The steady-state visual evoked potential in vision research: A review. Norcia ; L. Gregory Appelbaum ; Justin M. Ales ; Benoit R. Cottereau ; Bruno Rossion. Author Affiliations. Andrews, St. Andrews, UK jma23 st-andrews. Journal of Vision May , Vol. Alerts User Alerts. The steady-state visual evoked potential in vision research: A review. You will receive an email whenever this article is corrected, updated, or cited in the literature. You can manage this and all other alerts in My Account.
This feature is available to authenticated users only. Get Citation Citation. Get Permissions. The goal of this article is to describe, at both the conceptual and technical levels, the core principles underlying a particular type of visual evoked potential—the steady-state visual evoked potential SSVEP —and its application to human sensory and cognitive processing.
Evoked potentials can be generated not only as a result of physical stimulation by a sensory stimulus exogenously generated evoked potentials but also by internal cognitive or motor processes endogenously generated evoked potentials. In their most common form, ERPs are recorded in response to an isolated, discrete stimulus event.
In contrast to these transient ERPs, exogenous ERPs can also be generated in response to a train of stimuli presented at a fixed rate. Because the responses to such periodic stimuli can be very stable in amplitude and phase over time, those responses have been referred to as the steady-state visually evoked potential Regan, Steady-state evoked potentials in response to visual stimuli were first reported by Adrian and Matthews a in a remarkable article that also demonstrated suppression of the alpha rhythm by attention.
More importantly for the purpose of this article, the technique has been extended to stimuli of increasing complexity, from luminance flicker to pictures of faces, and therefore this method has become more broadly applicable in visual science research. In this article, we describe the key features of the SSVEP and its generalization from single stimuli to multiple simultaneous stimuli.
In the process, we cover many different applications of the SSVEP for understanding visual perception and attention. At each stage, we point to prominent results that have been obtained with the method. We end with a discussion of work that has used the method in conjunction with computational modeling to understand nonlinear processing in the visual cortex.
When a single stimulus attribute is modulated periodically as a function of time, the evoked response generated by that stimulus has a periodic time course. While SSVEPs can be recorded at a wide range of frequencies, in most studies the stimulus frequency i.
At these high frequencies, the interval between stimuli is substantially shorter than the duration of the response that follows an individual stimulus presented in isolation, so that responses to individual stimuli overlap. Below this stimulation rate, responses to individual stimuli overlap but some features of the responses to each event remain preserved, such as responses at frequencies that are multiples of the stimulus frequency Heinrich, Here we consider that what is critical for the definition of a response as an SSVEP is not the temporal frequency of the stimulus but rather the fact that the stimulus and the response are each periodic.
Because the SSVEP response is periodic, it is confined to a specific set of frequencies, and it is thus natural to analyze it in the frequency domain instead of the time domain. The stimulus frequency determines the response frequency content: The response spectrum has narrowband peaks at frequencies that are directly related to the stimulus frequency. To distinguish stimulus frequencies from response frequencies, we will use the following notation: A capital letter F will be used to refer to a stimulus frequency, and a lowercase italic letter f will be used to refer to a response frequency.
This distinction in notation will become important when we discuss stimuli that contain more than one frequency e. The relationship between periodic signals in the frequency domain and their corresponding representation in the time domain is shown in Figure 1 , with the first column showing the signal waveform in the time domain and the second column showing the spectrum, which is the corresponding representation of the signal in the frequency domain.
The example in Figure 1a consists of the simplest case—a periodic signal consisting of a single sine wave. The sine wave shown has a frequency F of 2 Hz. In the time domain, there are two peaks and troughs over a period of 1 s.
In the frequency domain Figure 1b , the response consists of a single line i. Figure 1. View Original Download Slide. Conceptual illustration of steady-state responses in time and frequency domains. Figure 1 Conceptual illustration of steady-state responses in time and frequency domains.
An important concept in spectrum analysis is the notion of spectral resolution. Spectral resolution is the fineness of the bins on the frequency axis of the spectrum.
The frequency resolution is inversely proportional to the duration of the electroencephalograph EEG segment that is transformed to the frequency domain. The spectrum analysis requires that at least one full period of the response of interest be present.
Spectrum resolution is important in determining the signal-to-noise ratio SNR of the measurement and in being able to separately analyze responses that have multiple frequency components in them see Multiple periodic visual inputs. The phase value is related to processing delays in the visual system and is a composite of temporal integration times in the retina and cortex and temporal propagation delays between retina and cortex and between areas in cortex.
The response amplitude and phase can be represented as a vector in a polar coordinate system see Figure 1c and f. The length of the vector codes the response amplitude, and the polar angle codes the response phase. In Figure 1 , the origin for the phase parameter is at 3 o'clock, where the phase is 0. The waveform in Figure 1a has a phase of 0, consistent with it being a cosine wave with its peak at time 0. The next row Figure 1d through f shows another simulated response with the same amplitude but a different temporal delay.
Here the delay corresponds to one quarter of a cycle compare Figure 1a and c. The amplitude and frequency of the response are the same and thus the amplitude spectrum is identical Figure 1b and e.
The relationship between phase and temporal delay is discussed in detail in Appendix 1. This occurs because either because the stimulus contains multiple temporal frequencies as when a square-wave temporal modulation profile is used or the system is nonlinear, or both.
A harmonically related response component is one that occurs at an exact integer multiple of the stimulus frequency—2 f , 3 f , and so on—where the use of a lowercase f indicates that what is being referred to is a response frequency rather than a stimulus frequency F.
The case of multiple response frequencies is illustrated with synthetic data in Figure 1g , where we again show a periodic signal that repeats exactly two times per second, as did the sine-wave signal in Figure 1a. This signal, however, contains additional frequency components that are readily apparent in the spectrum shown in Figure 1h.
Here we see additional response components at 2 2 f and 3 times 3 f the input frequency F. These higher harmonic components distort the shape of the time-domain waveform away from that of a single sine-wave profile.
If the visual stimulus is a perfect sine wave, it does not contain higher harmonics; if the visual response is linear, the response to this stimulus will be confined to the frequency bin corresponding to the stimulus frequency. In contrast, if the system is nonlinear, this will manifest in the presence of higher harmonic responses for more details on nonlinearity in the SSVEP, see Multi-input interactions as an objective measurement of system nonlinearities and neural convergence.
The nonlinear nature of the visual response is illustrated in Figure 1i , which shows data from an actual SSVEP recording where the stimulus comprised a sinusoidal modulation of contrast at a frequency F of 7. Here again, the time waveform is periodic but not sinusoidal, and the response spectrum thus contains narrow lines at exact integer multiples of the input frequency Figure 1j. The presence of frequencies in the response the output that were not present in the stimulus the input indicates that the response of the visual system is due to the activity of nonlinear neural mechanisms.
The experimental noise is present over all frequencies in the spectrum white bars in Figure 1j , with more noise in low frequencies and in specific broadband frequency ranges such as the alpha band 8—12 Hz; see, e. By contrast, the SSVEP signal that one seeks to isolate experimentally is confined to a set of narrow frequency bins that are directly related to the stimulus frequency.
This means that the SNR of the SSVEP can thus be very high, because only a small fraction of the noise—the noise that is present in the same bins as the response—is relevant Regan, Appendix 2 provides technical details on statistical analysis procedures appropriate for determining when an SSVEP is present and distinguishable from the background noise.
In a seminal study, Regan reported a maximal response at about 10 Hz for luminance flicker. Typically, studies such as these have used low-level visual stimuli and recordings from medial occipital sites. However, the stimulation frequency that gives rise to the largest SSVEP response may depend on the kind of stimulus used and the recording site Srinivasan et al.
Under the hypothesis that the stimulation rate generating the largest SSVEP is inversely related to the time needed to fully process the stimulus, lower stimulation rates may be necessary to record SSVEPs generated by higher level visual processes, for instance the discrimination of complex stimuli such as faces i.
As noted already—and this is an important point—what is critical for the definition of a response as an SSVEP is not the temporal frequency of the stimulus but rather the fact that the stimulus and the response are each periodic. In this paradigm, the SSVEP is measured in response to a stimulus that is parametrically varied swept over a range of values, rather than being presented at a fixed, unchanging value Regan, Spatial acuity is measured by sweeping the spatial frequency e.
Similarly, contrast sensitivity can be measured by varying the contrast of a fixed-spatial-frequency pattern over a wide range of contrast values. Measurement of contrast response and spatial frequency tuning functions is illustrated in Figure 2.
On the left is a contrast response function solid red curve that was measured by increasing the contrast of a binary noise pattern between 0. The sweeps each lasted 10 s, and the data are from the average of 10 of these s trials.
The response at 5. This behavior can be seen in the data of Figure 2a.
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