Brain-Computer Interface for Generating Personally Attractive Images
نویسندگان
چکیده
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 mimic complex data distributions, can potentially model subjective preferences unconstrained by pre-defined parameterization. Here, present generative brain-computer interfaces (GBCI), coupling GANs with interfaces. GBCI first presents selection images captures personalized attractiveness reactions toward the via electroencephalography. These are then used control GAN model, finding representation matches features constituting an attractive image for individual. We conducted experiment (N = 30) validate using face-generating producing hypothesized be attractive. In double-blind evaluation GBCI-produced against matched controls, found yielded highly accurate results. Thus, use EEG responses valid tool interactive information-generation. Furthermore, GBCI-derived visually replicated known effects from social neuroscience, suggesting responsive, nature provides powerful, new in mapping individual differences visualizing cognitive-affective processing.
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ژورنال
عنوان ژورنال: IEEE Transactions on Affective Computing
سال: 2023
ISSN: ['1949-3045', '2371-9850']
DOI: https://doi.org/10.1109/taffc.2021.3059043