Product semantics translation from brain activity via adversarial learning
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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.
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.
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References · 7
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,039
- 2017Semantic Image Synthesis via Adversarial Learningcited by 87
- 2017Deep learning with convolutional neural networks for decoding and visualization of EEG pathologycited by 13
- 2021A General Framework for Revealing Human Mind with auto-encoding GANscited by 3
- 2018Implementation of Deep Convolutional Neural Network in Multi-class Categorical Image Classificationcited by 1
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