Nonlinear dynamics of cortical responses to color in the human cVEP

Chromatic visual evoked potentials: A review of physiology, methods and clinical applications

Objective assessment of the visual system can be performed electrophysiologically using the visual evoked potential (VEP). In many clinical circumstances, this is performed using high contrast achromatic patterns or diffuse flash stimuli. These methods are clinically valuable but they may only assess a subset of possible physiological circuitries within the visual system, particularly those involved in achromatic (luminance) processing. The use of chromatic VEPs (cVEPs) in addition to standard VEPs can inform us of the function or dysfunction of chromatic pathways. The chromatic VEP has been well studied in human health and disease. Yet, to date our knowledge of their underlying mechanisms and applications remains limited. This likely reflects a heterogeneity in the methodology, analysis and conclusions of different works, which leads to ambiguity in their clinical use. This review sought to identify the primary methodologies employed for recording cVEPs. Furthermore cVEP maturation and application in understanding the function of the chromatic system under healthy and diseased conditions are reviewed. We first briefly describe the physiology of normal colour vision, before describing the methodologies and historical developments which have led to our understanding of cVEPs. We thereafter describe the expected maturation of the cVEP, followed by reviewing their application in several disorders: congenital colour vision deficiencies, retinal disease, glaucoma, optic nerve and neurological disorders, diabetes, amblyopia and dyslexia. We finalise the review with recommendations for testing and future directions.

Low luminance contrast's effect on the color appearance of S-cone patterns

There is a surprisingly strong effect on color appearance when low levels of luminance contrast are added to visual targets in which only S-cones are modulated. This phenomenon can be studied with checkerboard patterns composed of alternating S-cone-modulated checks and gray checks. + S checks look purple when surrounded by slightly brighter gray checks but look highly desaturated (lavender, almost white) when surrounded by darker gray checks. -S checks change in hue with luminance contrast; they look yellow when surrounded by darker gray checks but are greener when surrounded by lighter checks. Psychophysical paired comparisons confirm these perceptions. Furthermore, visual evoked potentials (VEPs) recorded from human posterior cortex indicate that signals evoked by low luminance contrast interact nonlinearly with S-cone-evoked signals in early cortical color processing. Our new psychophysics and electrophysiology results prove that human perception of color appearance is not based on neural computations within a separate, isolated color system. Rather, signals evoked by color contrast and luminance contrast interact to produce the colors we see.

Continuous Detection of Stimulus Brightness Differences Using Visual Evoked Potentials in Healthy Volunteers with Closed Eyes

Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulations, and between light stimulations with different brightnesses in awake volunteers with closed eyelids. This new method utilizing EEG analysis is visionary in the understanding of visual signal processing and will facilitate the deepening of our knowledge concerning anesthetic research. Methods: X-gradient boosting models were used to classify the cortical response to visual stimulation (no light vs. light stimulations and two lights with different brightnesses). For each of the two classifications, three scenarios were tested: training and prediction in all participants (all), training and prediction in one participant (individual), and training across all but one participant with prediction performed in the participant left out (one out). Results: Ninety-four Caucasian adults were included. The machine learning algorithm had a very high predictive value and accuracy in differentiating between no light and any light stimulations (AUCROCall: 0.96; accuracyall: 0.94; AUCROCindividual: 0.96 ± 0.05, accuracyindividual: 0.94 ± 0.05; AUCROConeout: 0.98 ± 0.04; accuracyoneout: 0.96 ± 0.04). The machine learning algorithm was highly predictive and accurate in distinguishing between light stimulations with different brightnesses (AUCROCall: 0.97; accuracyall: 0.91; AUCROCindividual: 0.98 ± 0.04, accuracyindividual: 0.96 ± 0.04; AUCROConeout: 0.96 ± 0.05; accuracyoneout: 0.93 ± 0.06). The predictive value and accuracy of both classification tasks was comparable between males and females. Conclusions: Machine learning algorithms could almost continuously and reliably differentiate between the cortical EEG responses to no light or light stimulations using visual evoked potentials in awake female and male volunteers with eyes closed. Our findings may open new possibilities for the use of visual evoked potentials in the clinical and intraoperative setting.

Decoding of EEG signals reveals non-uniformities in the neural geometry of colour

The idea of colour opponency maintains that colour vision arises through the comparison of two chromatic mechanisms, red versus green and yellow versus blue. The four unique hues, red, green, blue, and yellow, are assumed to appear at the null points of these the two chromatic systems. Here we hypothesise that, if unique hues represent a tractable cortical state, they should elicit more robust activity compared to other, non-unique hues. We use a spatiotemporal decoding approach to report that electroencephalographic (EEG) responses carry robust information about the tested isoluminant unique hues within a 100-350 ms window from stimulus onset. Decoding is possible in both passive and active viewing tasks, but is compromised when concurrent high luminance contrast is added to the colour signals. For large hue-differences, the efficiency of hue decoding can be predicted by mutual distance in a nominally uniform perceptual colour space. However, for small perceptual neighbourhoods around unique hues, the encoding space shows pivotal non-uniformities which suggest that anisotropies in neurometric hue-spaces may reflect perceptual unique hues.

Signals from Single-Opponent Cortical Cells in the Human cVEP

We used the chromatic visual evoked potential (cVEP) to study responses in human visual cortex evoked by equiluminant color stimuli for 6 male and 11 female observers. Large-area, colored squares were used to stimulate Single-Opponent cells preferentially, and fine color-checkerboard stimuli were used to activate Double-Opponent responses preferentially. Stimuli were modulated along the following two directions in color space: (1) the cardinal direction, L-M or M-L of DKL (Derrington, Krauskopf, and Lennie) space; and (2) the line from the white point to the color of the Red LED in the display screen, which was approximately intermediate between the L-M and -S directions in DKL space in cone-contrast coordinates. The amplitudes of cVEPs to large squares were smaller than those to checkerboards, and the latency of the cVEP response to squares was significantly less than the checkerboard latency. The latency of cVEP responses to the squares varied little with cone-contrast unlike the steep reduction of latency with cone-contrast observed in responses to color checkerboard patterns. The dynamic differences between cVEPs to squares and checkerboards support the hypothesis that a distinct neuronal mechanism responded to squares: Single-Opponent cells. Response amplitude, latency, and transientness-and their dependence on cone-contrast-were similar in the responses in the L-M and Red color directions. The similarity supports the hypothesis that the Single-Opponent signals in the cVEP come from a distinct population of cells that receives subtractive inputs from L and M cones, either L-M or M-L.SIGNIFICANCE STATEMENT This article is about characterizing the visual behavior of a distinct population of neurons in the human visual cortex, the Single-Opponent color cells. Based on single-cell results in the visual cortex of macaque monkeys, we used large uniformly colored stimuli to isolate the responses of Single-Opponent cells in the chromatic visual evoked potential (cVEP) recorded on the scalp of human observers. VEP signals recorded under conditions believed to reveal Single-Opponent responses are small and transient. Their time course is relatively unaffected by cone-contrast, and they are relatively insensitive to stimulus modulation of short wavelength-sensitive S cones. Because Single-Opponent cells convey signals that can be used to judge the color of scene illumination, knowing their visual properties is important for understanding color vision.

Luminance Contrast Drives Interactions between Perception and Working Memory

Visual working memory (WM) enables the use of past sensory experience in guiding behavior. Yet, laboratory tasks commonly evaluate WM in a way that separates it from its sensory bottleneck. To understand how perception interacts with visual memory, we used a delayed shape recognition task to probe how WM may differ for stimuli that bias processing toward different visual pathways. Luminance compared with chromatic signals are more efficient in driving the processing of shapes and may thus also lead to better WM encoding, maintenance, and memory recognition. To evaluate this prediction, we conducted two experiments. In the first psychophysical experiment, we measured contrast thresholds for different WM loads. Luminance contrast was encoded into WM more efficiently than chromatic contrast, even when both sets of stimuli were equated for discriminability. In the second experiment, which also equated stimuli for discriminability, early sensory responses in the EEG that are specific to luminance pathways were modulated by WM load and thus likely reflect the neural substrate of the increased efficiency. Our results cannot be accounted for by simple saliency differences between luminance and color. Rather, they provide evidence for a direct connection between low-level perceptual mechanisms and WM by showing a crucial role of luminance for forming WM representations of shape.

A multiplicity of color-responsive cortical mechanisms revealed by the dynamics of cVEPs

Our results connect higher-order color mechanisms deduced from psychophysics with the known diversity of populations of double-opponent, color-responsive cells in V1. We used the chromatic visual evoked potential, the cVEP, to study responses in human visual cortex to equiluminant color patterns. Stimuli were modulated along three directions in color space: the cardinal directions, L-M and S, and along the line in color space from the white point to the color of the Red LED in the display screen (the Red direction). The Red direction is roughly intermediate between L-M and S in DKL space in cone-contrast coordinates. While cVEP response amplitude, latency, and width--and their dependences on cone contrast-- were similar in the L-M and Red directions, the Transientness of the Red response was significantly greater than for responses to stimuli in the L-M direction and in the S direction. This difference in response dynamics supports the concept that there are multiple, distinct neuronal populations, so-called higher- order color mechanisms, for color perception within human V1 cortex.

Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction

Code modulated Visual Evoked Potentials (c-VEP) based BCI studies usually employ m-sequences as a modulating codes for their broadband spectrum and correlation property. However, subjective fatigue of the presented codes has been a problem. In this study, we introduce chaotic codes containing broadband spectrum and similar correlation property. We examined whether the introduced chaotic codes could be decoded from EEG signals and also compared the subjective fatigue level with m-sequence codes in normal subjects. We generated chaotic code from one-dimensional logistic map and used it with conventional 31-bit m-sequence code. In a c-VEP based study in normal subjects (n = 44, 21 females) we presented these codes visually and recorded EEG signals from the corresponding codes for their four lagged versions. Canonical correlation analysis (CCA) and spatiotemporal beamforming (STB) methods were used for target identification and comparison of responses. Additionally, we compared the subjective self-declared fatigue using VAS caused by presented m-sequence and chaotic codes. The introduced chaotic code was decoded from EEG responses with CCA and STB methods. The maximum total accuracy values of 93.6 ± 11.9% and 94 ± 14.4% were achieved with STB method for chaotic and m-sequence codes for all subjects respectively. The achieved accuracies in all subjects were not significantly different in m-sequence and chaotic codes. There was significant reduction in subjective fatigue caused by chaotic codes compared to the m-sequence codes. Both m-sequence and chaotic codes were similar in their accuracies as evaluated by CCA and STB methods. The chaotic codes significantly reduced subjective fatigue compared to the m-sequence codes.

Cortical Double-Opponent Cells in Color Perception: Perceptual Scaling and Chromatic Visual Evoked Potentials

In the early visual cortex V1, there are currently only two known neural substrates for color perception: single-opponent and double-opponent cells. Our aim was to explore the relative contributions of these neurons to color perception. We measured the perceptual scaling of color saturation for equiluminant color checkerboard patterns (designed to stimulate double-opponent neurons preferentially) and uniformly colored squares (designed to stimulate only single-opponent neurons) at several cone contrasts. The spatially integrative responses of single-opponent neurons would produce the same response magnitude for checkerboards as for uniform squares of the same space-averaged cone contrast. However, perceived saturation of color checkerboards was higher than for the corresponding squares. The perceptual results therefore imply that double-opponent cells are involved in color perception of patterns. We also measured the chromatic visual evoked potential (cVEP) produced by the same stimuli; checkerboard cVEPs were much larger than those for corresponding squares, implying that double-opponent cells also contribute to the cVEP response. The total Fourier power of the cVEP grew sublinearly with cone contrast. However, the 6-Hz Fourier component's power grew linearly with contrast-like saturation perception. This may also indicate that cortical coding of color depends on response dynamics.