Mental Fatigue Prediction from Multi-Channel ECOG Signal
Lin Yao, Jonathan L. Baker, Jae-Wook Ryou, Nicholas D. Schiff, Keith P. Purpura, Mahsa Shoaran
Abstract
Early detection of mental fatigue and changes in vigilance could be used to initiate neurostimulation to treat patients suffering from brain injury and mental disorders. In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two non-human primates (NHPs) as they performed a cognitively demanding task over extended periods of time. We employed a set of biomarkers to identify mental fatigue and a gradient boosting classifier to predict the performance outcome, seconds prior to the actual behavior response. An average F1 score of 75.4% ± 8.4% and 86.4%±6.6% was obtained for the two studied NHPs. Our preliminary results demonstrate the feasibility of detecting mental fatigue in healthy primates that could be used for closed-loop control of neurostimulation therapy.
BibTeX
@inproceedings{icassp2020_mentalfatiguepre,
title = {Mental Fatigue Prediction from Multi-Channel ECOG Signal},
author = {Lin Yao and Jonathan L. Baker and Jae-Wook Ryou and Nicholas D. Schiff and Keith P. Purpura and Mahsa Shoaran},
booktitle = {ICASSP 2020},
year = {2020}
}