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.

Neurons in the primary visual cortex are selective for the size, orientation and direction of motion of patterns falling within a restricted region of visual space known as the receptive field. The response to stimuli presented within the receptive field can be facilitated or suppressed by other stimuli falling outside the receptive field which, when presented in isolation, fail to activate the cell. Whether this interaction is facilitative or suppressive depends on the relative orientation of pattern elements inside and outside the receptive field. Here we show that neuronal facilitation preferentially occurs when a near-threshold stimulus inside the receptive field is flanked by higher-contrast, collinear elements located in surrounding regions of visual space. Collinear flanks and orthogonally oriented flanks, however, both act to reduce the response to high-contrast stimuli presented within the receptive field. The observed pattern of facilitation and suppression may be the cellular basis for the observation in humans that the detectability of an oriented pattern is enhanced by collinear flanking elements. Modulation of neuronal responses by stimuli falling outside their receptive fields may thus represent an early neural mechanism for encoding objects and enhancing their perceptual saliency.

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Event-related brain potentials (ERPs) in response to tachistoscopically presented photographs of 2 human faces were recorded for 4--7-month-old infants. For each infant 1 face was chosen to be presented frequently (p = .88, a low-information event) and the other infrequently (p = .12, a high-information event). Both types of events elicited in our infants a long-latency negative ERP wave (ca. 700 msec), termed Nc, and a long-latency positive wave (ca. 1,360 msec), termed Pc. We found that the discrepant, infrequently presented face elicited Nc waves which were higher in amplitude and longer in latency than those elicited by the frequent face. These differences suggest that our infants were able to remember the frequently presented face from trial to trial and to discriminate it from the discrepant face. The discrepant event elicited Pc waves which were insignificantly higher in amplitude than those elicited by frequent events. In adults and children, discrepant events have been found by numerous researchers to elicit positive P3 waves (latency ca. 300--800 msec). In our study, however, such waves could not be discerned. So, of all of the ERP waves which have been related to cognitive processes, the wave which is maturationally the earliest to appear is the Nc wave, which has been related to the perception of attention-getting events or events of interest to the subject. Our findings suggest that ERP responses could provide a sensitive means for investigating infant cognitive development since they do not depend upon an integrated motor-response system.

The recognition of object categories is effortlessly accomplished in everyday life, yet its neural underpinnings remain not fully understood. In this electroencephalography (EEG) study, we used single-trial classification to perform a Representational Similarity Analysis (RSA) of categorical representation of objects in human visual cortex. Brain responses were recorded while participants viewed a set of 72 photographs of objects with a planned category structure. The Representational Dissimilarity Matrix (RDM) used for RSA was derived from confusions of a linear classifier operating on single EEG trials. In contrast to past studies, which used pairwise correlation or classification to derive the RDM, we used confusion matrices from multi-class classifications, which provided novel self-similarity measures that were used to derive the overall size of the representational space. We additionally performed classifications on subsets of the brain response in order to identify spatial and temporal EEG components that best discriminated object categories and exemplars. Results from category-level classifications revealed that brain responses to images of human faces formed the most distinct category, while responses to images from the two inanimate categories formed a single category cluster. Exemplar-level classifications produced a broadly similar category structure, as well as sub-clusters corresponding to natural language categories. Spatiotemporal components of the brain response that differentiated exemplars within a category were found to differ from those implicated in differentiating between categories. Our results show that a classification approach can be successfully applied to single-trial scalp-recorded EEG to recover fine-grained object category structure, as well as to identify interpretable spatiotemporal components underlying object processing. Finally, object category can be decoded from purely temporal information recorded at single electrodes.

Here we expand on this work by investigating a class of textures that have maximal formal regularity, the 17 crystallographic wallpaper groups (Fedorov, 1891). We used texture stimuli from four of the groups that differ in the maximum order of rotation symmetry they contain, and measured neural responses in human participants using functional MRI and high-density EEG. We found that cortical area V3 has a parametric representation of the rotation symmetries in the textures that is not present in either V1 or V2, the first discovery of a stimulus property that differentiates processing in V3 from that of lower-level areas. Parametric responses were also seen in higherorder ventral stream areas V4, VO1, and lateral occipital complex (LOC), but not in dorsal stream areas. The parametric response pattern was replicated in the EEG data, and source localization indicated that responses in V3 and V4 lead responses in LOC, which is consistent with a feedforward mechanism. Finally, we presented our stimuli to four well developed feedforward models and found that none of them were able to account for our results. Our results highlight structural regularity as an important stimulus dimension for distinguishing the early stages of visual processing, and suggest a previously unrecognized role for V3 in the visual form-processing hierarchy.

Sighted animals extract motion information from visual scenes by processing spatiotemporal patterns of light falling on the retina. The dominant models for motion estimation exploit intensity correlations only between pairs of points in space and time. Moving natural scenes, however, contain more complex correlations. Here we show that fly and human visual systems encode the combined direction and contrast polarity of moving edges using triple correlations that enhance motion estimation in natural environments. Both species extract triple correlations with neural substrates tuned for light or dark edges, and sensitivity to specific triple correlations is retained even as light and dark edge motion signals are combined. Thus, both species separately process light and dark image contrasts to capture motion signatures that can improve estimation accuracy. This striking convergence argues that statistical structures in natural scenes have profoundly affected visual processing, driving a common computational strategy over 500 million years of evolution.