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 study, we show that it is also possible to track visual colour processing by using Linear Discriminant Analysis on patterns of EEG activity. 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. Finally, we show that while colour decoding can be sensitive to subtle differences in luminance, our colour decoding results are primarily driven by measured colour differences between stimuli. 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.
Highlights
Human scalp electroencephalography (EEG) and magnetoencephalography (MEG) are sensitive to synchronous activity in large neural populations, providing a macroscopic readout of brain activity
Decoding of visual-spatial features, which have typically been the focus in EEG decoding studies to date, is susceptible to contributions of spatial attention and/or eye movements, such as spatial biases in micro-saccades (Engbert and Kliegl, 2003; Hafed and Clark, 2002; Hollingworth et al, 2013; Mostert et al, 2018; Quax et al, 2019; Thielen et al, 2019; van Ede, Chekroud, and Nobre, 2019)
We found highly significant decodability for both visual features
Summary
Human scalp electroencephalography (EEG) and magnetoencephalography (MEG) are sensitive to synchronous activity in large neural populations, providing a macroscopic readout of brain activity. Fewer studies have successfully applied multivariate approaches to decode non-spatial features, such as colour. Decoding of visual-spatial features, which have typically been the focus in EEG decoding studies to date (such as orientation, location, and spatial frequency), is susceptible to contributions of spatial attention and/or eye movements, such as spatial biases in micro-saccades (Engbert and Kliegl, 2003; Hafed and Clark, 2002; Hollingworth et al, 2013; Mostert et al, 2018; Quax et al, 2019; Thielen et al, 2019; van Ede, Chekroud, and Nobre, 2019). The extension of decoding methods to features like colour, which are not defined by spatial parameters, provides an important validation of the approach by ensuring that decoding is based on the neural processing of feature-specific content. The demonstration of colour decoding from scalp EEG should have important implications – affording novel ways to track perceptual representations in various contexts
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