Code to accompany our International Conference on Robotics and Automation (ICRA) paper entitled - Using variable natural environment brain-computer interface stimuli for real-time humanoid robot navigation.
The original code for SSD is from https://github.com/amdegroot/ssd.pytorch.
The code is structured as follows:
onebox_stimuli.py
contains psychopy code for a simple SSVEP stimuli ;onebox_stimuli_RDA.py
contains code for integration between simple SSVEP stimuli with RDA server ;multiclass_stimuli.py
contains code for multiclass SSVEP stimuli -- 4 frequency classes example ;ssd_stimuli.py
contains code for displaying SSD object detection in psychopy as SSVEP stimuli;ssd.py
contains SSD architecture (original work from https://github.com/amdegroot/ssd.pytorch. ;ssd_predict.py
contains prediction test on the image frame modified from .https://github.com/amdegroot/ssd.pytorch/blob/master/demo/live.py. ;ssd_stimuli_realtime.py
our Varible SSVEP Stimuli code using SSD object detection in real time (on-board camera of NAO) ;
The code has been designed to support python 3.6+ only. The project has the following dependencies and version requirements:
- torch=1.6.0+
- numpy=1.16++
- python=3.6.5+
- scipy=1.1.0+
- PsychoPy=3.0
- pynaoqi=2.7+
- rdaclient (https://github.com/belevtsoff/rdaclient.py)
Please refer to https://github.com/amdegroot/ssd.pytorch on how to download the training weights.
Please cite the associated papers for this work if you use this code:
@inproceedings{aznan2019using,
title={Using variable natural environment brain-computer interface stimuli for real-time humanoid robot navigation},
author={Aznan, Nik Khadijah Nik and Connolly, Jason D and Al Moubayed, Noura and Breckon, Toby P},
booktitle={2019 International Conference on Robotics and Automation (ICRA)},
pages={4889--4895},
year={2019},
organization={IEEE}
}