Product semantics translation from brain activity via adversarial learning

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.

💡 Research Summary

This paper proposes a method to modify design semantics of a product from personalized brain activity using adversarial learning. The core idea is to develop a deep generative transformation model that can synthesize new features in a product image corresponding to EEG signals while maintaining other irrelevant image features. This approach aims at two main objectives: 1) synthesizing the product image with new features based on EEG signals, and 2) preserving the rest of the image’s characteristics unrelated to these signals.

The authors leverage adversarial learning and the concept from StarGAN to create a model capable of generating products with preferred design semantics (such as color and shape) directly from brain activity. A case study is conducted where shoes are generated with different design semantics based on recorded EEG signals, demonstrating how this cognitive transformation model can be applied in practice.

The results serve as proof-of-concept that the proposed framework has potential to synthesize product semantics from brain activity. This research opens up possibilities for personalized product development and enhanced user experience by directly incorporating brain signals into the design process. The study highlights the importance of small changes in design semantics on user satisfaction, emphasizing the need for more personalized approaches in product design.