Braincomputer Interface For Generatingpersonally Attractive Images Michiel Spap by Michiel Spapé, Keith M. Davis Iii, Lauri Kangassalo, Niklas Ravaja, Zania Sovijärvi-spapé, Tuukka Ruotsalo 19493045 instant download after payment.

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.