About: Neurocognition-inspired Design with Machine Learning

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #82586 Neurocognition-inspired Design with Machine Learning
sameAs	Neurocognition-inspired Design with Machine Learning
http://eprints.org/ontology/hasAccepted	Neurocognition-inspired Design with Machine Learning (PDF)
http://eprints.org/ontology/hasDocument	Neurocognition-inspired Design with Machine Learning (PDF) Neurocognition-inspired Design with Machine Learning (Other) Neurocognition-inspired Design with Machine Learning (Other) Neurocognition-inspired Design with Machine Learning (Other) Neurocognition-inspired Design with Machine Learning (Other) Neurocognition-inspired Design with Machine Learning (Other)
dc:hasVersion	Neurocognition-inspired Design with Machine Learning (PDF)
Title	Neurocognition-inspired Design with Machine Learning
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2020-12-17
Creator	Yike Guo Peter Childs Pan Wang L. Li Zhen Wei Shuo Wang Chao Wu Danlin Peng
status	peer reviewed published
Publisher	Cambridge University Press
abstract	Generating design via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). Firstly, we employ a recurrent neural network (LSTM - Long Short-Term Memory) as an encoder to extract EEG features from raw EEG signals; this data is recorded from subjects viewing several categories of images from ImageNet. Secondly, we train a GAN model conditioned on the encoded EEG features to generate design images. Thirdly, we use the model to generate design images from the subject’s EEG measured brain activity.
Is Part Of	Design Science https://kar.kent.ac.uk/id/repository
Subject	NK Decorative arts. Applied arts. Decoration and ornament Q Science (General)
list of authors	https://kar.kent.ac.uk/id/eprint/82586#authors
issue	e33
volume	6
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #82586 Neurocognition-inspired Design with Machine Learning

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