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Decoding chromaticity and luminance from patterns of EEG activity.

ABSTRACT: A long-standing question in the field of vision research is whether scalp-recorded EEG activity contains sufficient information to identify stimulus chromaticity. Recent multivariate work suggests that it is possible to decode which chromaticity an observer is viewing from the multielectrode pattern of EEG activity. There is debate, however, about whether the claimed effects of stimulus chromaticity on visual evoked potentials (VEPs) are instead caused by unequal stimulus luminances, which are achromatic differences. Here, we tested whether stimulus chromaticity could be decoded when potential confounds with luminance were minimized by (1) equating chromatic stimuli in luminance using heterochromatic flicker photometry for each observer and (2) independently varying the chromaticity and luminance of target stimuli, enabling us to test whether the pattern for a given chromaticity generalized across wide variations in luminance. We also tested whether luminance variations can be decoded from the topography of voltage across the scalp. In Experiment 1, we presented two chromaticities (appearing red and green) at three luminance levels during separate trials. In Experiment 2, we presented four chromaticities (appearing red, orange, yellow, and green) at two luminance levels. Using a pattern classifier and the multielectrode pattern of EEG activity, we were able to accurately decode the chromaticity and luminance level of each stimulus. Furthermore, we were able to decode stimulus chromaticity when we trained the classifier on chromaticities presented at one luminance level and tested at a different luminance level. Thus, EEG topography contains robust information regarding stimulus chromaticity, despite large variations in stimulus luminance.

SUBMITTER: Sutterer DW

PROVIDER: S-EPMC8111702 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities ("Neutral"), non-expressive movements while thinking about specific expressive qualities ("Think"), and enacted expressive movements ("Do"). The expressive movement qualities that were used in the "Think" and "Do" actions consisted of a sequence of eight Laban Effort qualities as defined by LMA-a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2-4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.

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Decoding chromaticity and luminance from patterns of EEG activity.

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