| id |
caadria2018_049 |
| authors |
Xu, Tongda, Wang, Dinglu, Yang, Mingyan, You, Xiaohui and Huang, Weixin |
| year |
2018 |
| title |
An Evolving Built Environment Prototype - A Prototype of Adaptive Built Environment Interacting with Electroencephalogram Supported by Reinforcement Learning |
| source |
T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 207-215 |
| doi |
https://doi.org/10.52842/conf.caadria.2018.2.207
|
| summary |
This paper proposes an environment prototype learning from people's Electroencephalogram (EEG) feedback in real-time. Instead of the widely adopted supervised learning method, a recently published affordable reinforcement learning model (PPO) is adopted to avoid bias from designers and to base the interaction on the subject and intelligent agent rather than between the designer and subject. In this way, development of interaction method towards a specific target is substantially accelerated. The target of this prototype is to keep the subject's alpha wave stable or decline, which indicated a more calming state, by intelligent decision of illumination state according to subject's EEG. The result is promising, a decent trained model could be gained within 500,000 steps facing this mid-complex environment. The target of keeping the alpha wave of subjects on a low or stable level purely by decision from computer agents is successfully reached. |
| keywords |
Brain–computer interface; Reinforcement learning; Adaptive environment; Electroencephalogram; Mindfulness training |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (4,259,590 bytes) |
| references |
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