Journal of Robotics, Networking and Artificial Life

Volume 8, Issue 2, September 2021, Pages 117 - 121

Development of EEG Data-driven Generative Art Application for Real-time and Dynamic Interaction

Authors
Chien-Tung Lin1, R.P.C. Janaka Rajapakse1, *, Yoshimasa Tokuyama2
1Graduate Institute of Animation and Film Art, Tainan National University of the Arts, No. 66, Daqi, Guantian Dist., Tainan City 72045, Taiwan
2Department of Media and Image Technology, Tokyo Polytechnic University, 1583, Iiyama, Kanagawa 243-0297, Japan
*Corresponding author. Email: janakaraja@gmail.com
Corresponding Author
R.P.C. Janaka Rajapakse
Received 4 December 2020, Accepted 7 May 2021, Available Online 23 July 2021.
DOI
10.2991/jrnal.k.210713.010How to use a DOI?
Keywords
EEG; phyllotaxis; generative art; interactive art; installation art
Abstract

Generative art is produced by procedural techniques. It has obtained a lot of attention since the beginning of computer graphics. Many works of art are inspired by nature, among which phyllotaxis is as well. It is a combination of mathematics and the beauty of nature. Not only can it be seen everywhere in nature, but also often appear in man-made objects, becoming part of culture or religion. This paper presents the development of an interactive generative art application that is created from a phyllotaxis pattern by using the user’s Electroencephalogram (EEG) data. When people are using it, it will allow them to more easily relax and achieve the function of art therapy. We tried to use EEG data to make an interactive installation art that creates phyllotaxis patterns that are projected on the wall. Everyone has a different state, the generated patterns are also different from person to person, which creates interesting interactive contents. In addition, sound can also be changed by EEG data to become dynamic and real-time contents.

Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
8 - 2
Pages
117 - 121
Publication Date
2021/07/23
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
10.2991/jrnal.k.210713.010How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Chien-Tung Lin
AU  - R.P.C. Janaka Rajapakse
AU  - Yoshimasa Tokuyama
PY  - 2021
DA  - 2021/07/23
TI  - Development of EEG Data-driven Generative Art Application for Real-time and Dynamic Interaction
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 117
EP  - 121
VL  - 8
IS  - 2
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.210713.010
DO  - 10.2991/jrnal.k.210713.010
ID  - Lin2021
ER  -