- Make sure your device has a python API
- Connect your device with python
- Call your device through CogBeacon's main.py (see lines 527-538)
- Write your one function that binds your device the the appropriate variables captured by CogBeacon (similarly to how it is done in function readMuse(), readFrames() and SelfReport() in main.py)
- Kivy Python Cross-Platform Gui library 1.9.0 or newer
Install Muse tools: http://dev.choosemuse.com/tools Install Python SDK Tools: http://das.nasophon.de/pyliblo/ Note: since we use 64-bit Linux OS, check the following link for MuseSDK on 64-bit system: http://forum.choosemuse.com/t/issues-running-muselab-and-muse-io/112/20 or https://github.com/elnn/tomato/blob/master/README.md
For additional help please contact the author at: michalis.papakostas@mavs.uta.edu or mpapakos@umich.edu
More instructions along with detailed documentation will be added soon.
Papakostas, Michalis, Akilesh Rajavenkatanarayanan, and Fillia Makedon. "CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue." Technologies 7.2 (2019): 46.
@article{papakostas2019cogbeacon, title={CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue}, author={Papakostas, Michalis and Rajavenkatanarayanan, Akilesh and Makedon, Fillia}, journal={Technologies}, volume={7}, number={2}, pages={46}, year={2019}, publisher={Multidisciplinary Digital Publishing Institute} }