Product semantics translation from brain activity via adversarial learning

TL;DR

This paper introduces a deep generative adversarial network model that translates brain activity signals into product design semantics, enabling the synthesis of product images with desired features based on EEG data.

Contribution

It presents a novel adversarial learning framework that modifies product semantics directly from brain signals, integrating EEG data with product image synthesis.

Findings

Proof-of-concept demonstrated with shoe design case study

Successfully synthesizes product images with targeted semantics from EEG signals

Framework shows potential for brain-driven product design customization

Abstract

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.