Decoding visual colour from scalp electroencephalography measurements

Abstract

Recent advances have made it possible to decode various aspects of visually presented stimuli from patterns of scalp EEG measurements. As of recently, such multivariate methods have been commonly used to decode visual-spatial features such as location, orientation, or spatial frequency. In the current proof-of-principle study, we show that it is also possible to track visual colour processing by using Linear Discriminant Analysis. Building on other recent demonstrations, we show that colour decoding: (1) reflects sensory qualities (as opposed to, for example, verbal labelling) with a prominent contribution from posterior electrodes contralateral to the stimulus, (2) conforms to a parametric coding space, (3) is possible in multi-item displays, and (4) is comparable in magnitude to the decoding of visual stimulus orientation. Through subsampling our data, we also provide an estimate of the approximate number of trials and participants required for robust decoding. Colour decoding opens a relevant new dimension in which to track visual processing using scalp EEG measurements, while bypassing potential confounds associated with decoding approaches that focus on spatial features.

Color Space Geometry Uncovered with Magnetoencephalography

“…15,16 We first show that stimulus color can be decoded using multivariate analyses of the spatial pattern of responses across the array of MEG sensors, confirming preliminary findings by ourselves and others. [17][18][19][20][21][22][23] We then examine the similarity relationships among the patterns of neural responses elicited by different colors. We judged colors to be more similar if they elicited more similar patterns of MEG activity.…”

Temporal dynamics of the neural representation of hue and luminance polarity

“…19). As with the luminancecontrast decoding problems, decoding had a higher peak magnitude for the identity problems (74% [69,78]) compared to the generalization problems (59% [55,64]), which provides support for the hypothesis that the brain also has a representation of hue that is inseparable from the representation of luminance contrast. The time to peak was not different for the identity and generalization problems (solid vertical line, 122 ms [110,130]; dashed vertical line, 123 ms [115,130]; p=0.584).…”

“…Here we explore a new approach to address the neural mechanisms for encoding hue and luminance contrast, using magnetoencephalography (MEG) in humans, coupled with multivariate analysis [44][45][46][47][48] (Figure 1). Color can be decoded from MEG activity [49][50][51][52][53][54][55][56] . In the present work, we exploit the exquisite temporal resolution of MEG to tease apart the neural mechanisms for hue and luminance contrast.…”

“…Color can be decoded from MEG activity (Hermann et al, 2015;Rosenthal et al, 2017;Sandhaeger et al, 2019;Teichmann et al, 2019;Hajonides et al, 2020;Hermann et al, 2020;Rosenthal et al, 2020a). We have previously shown that MEG data elicited by colors provide evidence of joint coding of hue and luminance contrast that reflect color-naming patterns and can be used to construct a geometry of the neural representation of color (Rosenthal et al, 2020b).…”

“…Here we explore another approach to address the neural mechanisms for encoding hue and luminance contrast, analyzing magnetoencephalography (MEG) in humans using multivariate analysis (Carlson et al, 2013; van de Nieuwenhuijzen et al, 2013; Cichy et al, 2014; Isik et al, 2014; Wardle et al, 2016) ( Figure 1 ). Color can be decoded from MEG activity(Hermann et al, 2015; Rosenthal et al, 2017; Sandhaeger et al, 2019; Teichmann et al, 2019; Hajonides et al, 2020; Hermann et al, 2020; Rosenthal et al, 2020a). We have previously shown that MEG data elicited by colors provide evidence of joint coding of hue and luminance contrast that reflect color-naming patterns and can be used to construct a geometry of the neural representation of color (Rosenthal et al, 2020b).…”