Key result
ECoG-based gradient boosting classifier predicts primate task performance with up to ~91% F1 score.
Why the study?
Detection and early prediction of mental fatigue could be used to adapt neuromodulation strategies to effectively treat patients suffering from brain injury and other indications with prominent chronic mental fatigue.
ECoG signals can be used to predict task performance and detect mental fatigue in non-human primates, suggesting potential for closed-loop neuromodulation strategies.
Supports ECoG performance prediction in primates; leaves open human translation and clinical validation.
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 periods of mental fatigue by analyzing ECoG signals, and that this general approach, in principle, could be used for closed-loop control of neuromodulation strategies.
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Yao et al. (2020) studied Mental fatigue (n=2). Higher order spectral-temporal (HOST) features of ECoG signals vs. Conventional spectral power features was evaluated on Classification performance (F1 score and accuracy) for predicting task performance. A gradient boosting classifier using higher order spectral-temporal ECoG features predicted task performance with average F1 scores of 77.5% and 91.2% in two non-human primates.
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