Evolutionary art with an EEG fitness function

Nĕmečková, Ingrid, James-Reynolds, Carl ORCID logoORCID: https://orcid.org/0000-0002-5892-5415 and Currie, Edward ORCID logoORCID: https://orcid.org/0000-0003-1186-5547 (2011) Evolutionary art with an EEG fitness function. Bramer, M. and Petridis, M., eds. Artificial Intelligence XXXVI 39th SGAI International Conference on Artificial Intelligence (AI-2019), Cambridge, UK, December 17–19, 2019, Proceedings. In: AI-2019 Thirty-ninth SGAI International Conference on Artificial Intelligence, 17-19 Dec 2019, Cambridge, United Kingdom. ISBN 9783030348847, e-ISBN 9783030348854. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-030-34885-4_19)

PDF - Final accepted version (with author's formatting)
Download (584kB) | Preview


This project involved the use of an interactive Genetic Al-gorithm (iGA) with an electroencephalogram (EEG)-based fitness function to create paintings in the style of Piet Mondrian, a Dutch painter who used geometric elements in his later paintings. Primary data for the prototype was gathered by analysis of twenty-seven existing Mondrian paintings. An EEG gaming headset was used to read EEG signals, which were transmitted by Bluetooth to an Arduino running an iGA. These values were used as the iGA fitness function. The data was sent to a PC running Processing to dis-play the artwork. The resultant displayed artwork evolves to favour higher attention and meditation levels, which are considered to represent greater mindfulness. The process ends when the observer identifies a piece of art they would like to keep. However, convergence of the algorithm is difficult to test as many parameters can affect the process. A number of issues aris-ing from the research are discussed and further work is proposed.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Proceedings part of the Lecture Notes in Computer Science book series (LNCS, volume 11927)
Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11927)
Research Areas: A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 27939
Notes on copyright: The final authenticated version is available online at https://doi.org/10.1007/978-3-030-34885-4_19
Useful Links:
Depositing User: Carl James-Reynolds
Date Deposited: 21 Oct 2019 20:36
Last Modified: 30 Nov 2022 00:43
URI: https://eprints.mdx.ac.uk/id/eprint/27939

Actions (login required)

View Item View Item


Activity Overview
6 month trend
6 month trend

Additional statistics are available via IRStats2.