Product semantics translation from brain activity via adversarial learning
This work addresses the challenge of translating brain activity into product design modifications for personalized user satisfaction, but it is incremental as it builds on existing adversarial learning methods like StarGAN.
The paper tackled the problem of modifying product design semantics (color and shape) based on personalized brain activity, using a deep generative model to synthesize product images with new features corresponding to EEG signals while maintaining other irrelevant features. The results serve as a proof-of-concept, demonstrating the framework's potential to generate product semantics from brain activity.
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.