Predicting task performance from biomarkers of mental fatigue in global brain activity.
Predicting task performance from biomarkers of mental fatigue in global brain activity.
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DOI：
10.1088/1741-2552/abc529
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发表时间：
2021-03-08
影响因子：
4
通讯作者：
Shoaran M
中科院分区：
文献类型：
--
作者：
Yao L;Baker JL;Schiff ND;Purpura KP;Shoaran M
精神疲劳的检测和早期预测（即警觉性变化）可用于调整神经调节策略，有效治疗脑损伤和其他慢性精神疲劳突出的患者。在这项研究中，我们分析了两只健康的非人灵长类动物（NHP）在长时间执行持续注意力任务时长期记录的皮质电图（ECoG）信号。我们使用一组ECoG信号的光谱时间和连通性生物标志物来识别精神疲劳的时间段，并使用梯度增强分类器来预测行为反应前几秒的表现。小波熵和瞬时幅值和频率是两个NHPs的最佳单特征。在两种NHPs中，使用高阶谱时（HOST）特征的分类性能显著高于常规谱功率特征。在分析的99个阶段中，每种动物的平均F1得分分别为77.5%±8.2%和91.2%±3.6%，分类器的准确率分别为79.5%±8.9%和87.6%±3.9%。我们的研究结果证明了通过分析ECoG信号来预测表现和检测精神疲劳时期的可行性，并且这种一般方法原则上可以用于神经调节策略的闭环控制。
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. 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. 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. 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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结合部分定向的连贯性和图形论，以分析不同心理任务的有效大脑网络。
影响因子：
2.9
作者：
Huang D;Ren A;Shang J;Lei Q;Zhang Y;Yin Z;Li J;von Deneen KM;Huang L
通讯作者：
Huang L
影响因子：
16.6
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通讯作者：
Kahana MJ
影响因子：
3.2
作者：
Benjamin AS;Fernandes HL;Tomlinson T;Ramkumar P;VerSteeg C;Chowdhury RH;Miller LE;Kording KP
通讯作者：
Kording KP
影响因子：
4
作者：
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通讯作者：
Tarokh, Vahid
DOI：
10.1109/34.574797
发表时间：
1997-02-01
影响因子：
23.6
作者：
Jain, A;Zongker, D
通讯作者：
Zongker, D