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https://ieeexplore.ieee.org/document/9353984Brain-Computer Interface for
Generating Personally Attractive ImagesAbstract:While we instantaneously
recognize a face as attractive, it is much harder to explain what exactly
defines personal attraction. This suggests that attraction depends on implicit
processing of complex, culturally and individually defined features. Generative
adversarial neural networks (GANs), which learn to mimic complex data
distributions, can potentially model subjective preferences unconstrained by
pre-defined model parameterization. Here, we present generative brain-computer
interfaces (GBCI), coupling GANs with brain-computer interfaces.GBCI first
presents a selection of images and captures personalized attractiveness
reactions toward the images via electroencephalography. These reactions are then
used to control a GAN model, finding a representation that matches the features
constituting an attractive image for an individual. We conducted an experiment
(N = 30) to validate GBCI using a face-generating GAN and producing images that
are hypothesized to be individually attractive. In double-blind evaluation of
the GBCI-produced images against matched controls, we found GBCI yielded highly
accurate results. Thus, the use of EEG responses to control a GAN presents a
valid tool for interactive information-generation. Furthermore, the GBCI-derived
images visually replicated known effects from social neuroscience, suggesting
that the individually responsive, generative nature of GBCI provides a powerful,
new tool in mapping individual differences and visualizing cognitive-affective
processing.

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