(2017). A mental fatigue index based on regression using mulitband EEG features with application in simulated driving. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3220–3223). IEEE. https://doi.org/10.1109/embc.2017.8037542
Περίληψη
Development of accurate fatigue level prediction models is of great importance for driving safety. In parallel, a limited number of sensors is a prerequisite for development of applicable wearable devices. Several EEG-based studies so far have performed classification in two or few levels, while others have proposed indices based on power ratios. Here, we utilized a regression Random Forest model in order to provide more accurate continuous fatigue level prediction. In detail, multiband power features were extracted from EEG data recorded from one hour simulated driving task. Next, cross-subject regression was performed to obtain common fatigue-related discriminative features. We achieved satisfactory prediction accuracy and simultaneously we minimized required electrodes, proposing to use a set of 3 electrodes.
- DOI
- 10.1109/embc.2017.8037542
- Τύπος
- Άρθρο σε Πρακτικά Συνεδρίου
- Έτος
- 2017
Σύνδεσμοι
BibTeX
@inproceedings{dimitrakopoulos2017mental,
title = {A mental fatigue index based on regression using mulitband EEG features with application in simulated driving},
author = {Georgios N. Dimitrakopoulos and Ioannis Kakkos and Nitish V. Thakor and Anastasios Bezerianos and Yu Sun},
url = {https://doi.org/10.1109/embc.2017.8037542},
doi = {10.1109/embc.2017.8037542},
year = {2017},
date = {2017-01-01},
booktitle = {2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
volume = {2017},
pages = {3220–3223},
publisher = {IEEE},
abstract = {Development of accurate fatigue level prediction models is of great importance for driving safety. In parallel, a limited number of sensors is a prerequisite for development of applicable wearable devices. Several EEG-based studies so far have performed classification in two or few levels, while others have proposed indices based on power ratios. Here, we utilized a regression Random Forest model in order to provide more accurate continuous fatigue level prediction. In detail, multiband power features were extracted from EEG data recorded from one hour simulated driving task. Next, cross-subject regression was performed to obtain common fatigue-related discriminative features. We achieved satisfactory prediction accuracy and simultaneously we minimized required electrodes, proposing to use a set of 3 electrodes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}