  A small change of design semantics may affect a user's satisfaction with a
product. To modify a design semantic of a given product from personalised brain
activity via adversarial learning, in this work, we propose a deep generative
transformation model to modify product semantics from the brain signal. We
attempt to accomplish such synthesis: 1) synthesising the product image with
new features corresponding to EEG signal; 2) maintaining the other image
features that irrelevant to EEG signal. We leverage the idea of StarGAN and the
model is designed to synthesise products with preferred design semantics
(colour & shape) via adversarial learning from brain activity, and is applied
with a case study to generate shoes with different design semantics from
recorded EEG signals. To verify our proposed cognitive transformation model, a
case study has been presented. The results work as a proof-of-concept that our
framework has the potential to synthesis product semantic from brain activity.
