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Citation |
Yao L, Baker JL, Schiff ND, Purpura K, Shoaran M. J. Neural. Eng. 2020; ePub(ePub): ePub. |
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Copyright |
(Copyright © 2020, Institute of Physics Publishing) |
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DOI |
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PMID |
33108778 |
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Abstract |
OBJECTIVE Detection and early prediction of mental fatigue (i.e., shifts in vigilance), could be used to adapt neuromodulation strategies to effectively treat patients suffering from brain injury and other indications with prominent chronic mental fatigue. Approach In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two healthy non-human primates (NHP) as they performed a sustained attention task over extended periods of time. We employed a set of spectrotemporal and connectivity biomarkers of the ECoG signals to identify periods of mental fatigue and a gradient boosting classifier to predict performance, up to several seconds prior to the behavioral response. Main results Wavelet entropy and the instantaneous amplitude and frequency were among the best single features across sessions in both NHPs. The classification performance using higher order spectral-temporal (HOST) features was significantly higher than that of conventional spectral power features in both NHPs. Across the 99 sessions analyzed, average F1 scores of 77.5%±8.2% and 91.2%±3.6%, and accuracy of 79.5%±8.9% and 87.6%±3.9% for the classifier were obtained for each animal, respectively. Significance Our results here demonstrate the feasibility of predicting performance and detecting mental fatigue by analyzing ECoG signals, and that this general approach, in principle, could be used for closed-loop control of neuromodulation strategies. Language: en |
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Keywords |
Machine Learning; Traumatic Brain Injury; Mental Fatigue; ECoG; Non-human Primates; Vigilance Task |