In this paper, we explore whether Attention-deficit/hyperactivity disorder (ADHD) can be detected based on recorded eye movements together with information about the video stimulus in a free-viewing task
Clone repository:
git clone git@github.com:aeye-lab/ecml-ADHD
or
git clone https://github.com/aeye-lab/ecml-ADHD
and change to the cloned repo via cd ecml-ADHD
.
Install dependencies:
pip install -r requirements.txt
Please download videos from http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/EEG-Eyetracking%20Protocol.html, and break it down into frames (e.g. use ffmpeg packages) and put them under the folder /Data/videos/frames_{video_name}/
We use a state-of-the-art saliency model, DeepGazeII to compute salinecy maps for our video stimuli. Download the files you need to run the DeepGazeII model https://drive.google.com/file/d/1kYUwoatqQUS5EabeeSDc6gRmCysnVZ6N/view, and stored under the folder /DNN_model/DataGeneration/
To generate the saliency maps for all videos, run
bash gen_saliency_map_data.sh
To generate model input files, run
bash gen_model_input_data.sh
bash run_models.sh
If you use our code for your research, please consider citing our paper:
@inproceedings{deng2023detection,
title={Detection of ADHD based on eye movements during natural viewing},
author={Deng, Shuwen and Prasse, Paul and Reich, David R and Dziemian, Sabine and Stegenwallner-Sch{\"u}tz, Maja and Krakowczyk, Daniel and Makowski, Silvia and Langer, Nicolas and Scheffer, Tobias and J{\"a}ger, Lena A},
booktitle={Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19--23, 2022, Proceedings, Part VI},
pages={403--418},
year={2023},
organization={Springer}
}