# Product semantics translation from brain activity via adversarial learning

arXiv: https://arxiv.org/abs/2103.15602 · HTML: https://mlsanity.com/paper/2103.15602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Semantic Image Synthesis via Adversarial Learning](https://mlsanity.com/paper/1707.06873/) (2017)
- [Deep learning with convolutional neural networks for decoding and visualization of EEG pathology](https://mlsanity.com/paper/1708.08012/) (2017)
- [A General Framework for Revealing Human Mind with auto-encoding GANs](https://mlsanity.com/paper/2102.05236/) (2021)
- [Implementation of Deep Convolutional Neural Network in Multi-class Categorical Image Classification](https://mlsanity.com/paper/1801.01397/) (2018)

## Abstract (arXiv)

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.