Abstract
This project involved the use of an interactive Genetic Algorithm (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 display 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 arising from the research are discussed and further work is proposed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Blotkamp, C.: Mondrian: The Art of Destruction. Reaktion Books (2001). ISBN: 9781861891006
De Jong, C.: Piet Mondrian: Life and Work. Abrams (2015). ISBN: 9781419714085. Knuth
Bris-Marino, P.: La influencia de la teosofía sobre la obra neoplástica de Mondrian. Arte, Individuo y Sociedad 26 (2014). https://doi.org/10.5209/rev_ARIS.2014.v26.n3.42960
Foundation, The Art Story: De Stijl (2019). https://www.theartstory.org/movement-de-stijl.htm. Accessed 12 Nov 2018
Edmonds, E.: Interactive Art (n.d.). https://pdfs.semanticscholar.org/e245/971a.pdf. Accessed 15 Nov 2018
Tempel, M.: Generative art for all. J. Innov. Entrep. 6 (2017). https://doi.org/10.1186/s13731-017-0072-1
Cohen, H.: 1988-How to Draw Three People in a Botanical Garden 10 (n.d.)
Sheridan, S.L.: Mind/senses/hand: the generative systems program at the art institute of Chicago 1970-1980. Leonardo 23, 175 (1990). https://doi.org/10.2307/1578602
Schwartz, L.F.: Art Analysis - 1984 THE HIDDEN MONALISA (1984). http://lillian.com/art-analysis/. Accessed 20 Nov 2018
Reas, C., Fry, B.: Processing: A Programming Handbook for Visual Designers and Artists. The MIT Press, Cambridge (2015). ISBN: 9780262321853
Arduino Platform. https://www.arduino.cc/. Accessed 20 June 2019
Fee, D.: Supplementing Fine Art Education with Digital Interactivity 49 (n.d.)
Shen, J.Y., Gedeon, T.: Cyber-Genetic Neo-Plasticism – an AI program creating Mondrian-like paintings by using interactive bacterial evolution algorithm (2007). http://cs.anu.edu.au/escience/project/06S2/report/JianShen_report.pdf
Melanie, M.: An Introduction to Genetic Algorithms 162 (n.d.)
Banzhaf, W., et al.: Genetic Programming Arduino (2019). Arduino. https://www.arduino.cc/. Accessed 02 Dec 2018
Darwin, C.: On The Origin of Species by Means of Natural Selection, or Preservation of Favoured Races in the Struggle for Life, p. 1859. John Murray, London (1809–1882)
Hudson, D.L., Cohen, M.E.: Neural Networks and Artificial Intelligence for Biomedical Engineering. IEEE Press Series in Biomedical Engineering. Institute of Electrical and Electronics Engineers, New York (2000)
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms, 2nd edn. Wiley, Hoboken (2004)
Thierens, D., Goldberg, D.: Convergence models of genetic algorithm selection schemes. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 119–129. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-58484-6_256
Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst. Man Cybern. 24(4), 656–667 (1994)
WillPhelps1. https://gist.github.com/WillPhelps1/4da7f9b2eb718340bedf885b63c9f729. Accessed 02 July 2019
Google Cloud: Vision API (2018). https://cloud.google.com/vision/. Accessed 27 December 2018
James-Reynolds, C., Currie, E.: EEuGene: employing electroencephalograph signals in the rating strategy of a hardware-based interactive genetic algorithm. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXXIII, pp. 343–353. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47175-4_25
NeuroSky: NeuroSky Brainwave Starter Kit (2018). https://store.neurosky.com/pages/mindwave. Accessed 01 Feb 2019
Microchip RN-41. https://www.microchip.com/wwwproducts/en/RN41. Accessed 02 July 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nĕmečková, I., James-Reynolds, C., Currie, E. (2019). Evolutionary Art with an EEG Fitness Function. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXVI. SGAI 2019. Lecture Notes in Computer Science(), vol 11927. Springer, Cham. https://doi.org/10.1007/978-3-030-34885-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-34885-4_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34884-7
Online ISBN: 978-3-030-34885-4
eBook Packages: Computer ScienceComputer Science (R0)