Although attention plays an important role in cognitive and perception, there is no simple way to measure one's attention abilities. We identified that the strength of brain functional network in sustained attention task can be used as the physiological indicator to predict behavioral performance. Behavioral and electroencephalogram (EEG) data from 14 subjects during three force control tasks were collected in this paper. The reciprocal of the product of force tolerance and variance were used to calculate the score of behavioral performance. EEG data were used to construct brain network connectivity by wavelet coherence method and then correlation analysis between each edge in connectivity matrices and behavioral score was performed. The linear regression model combined those with significantly correlated network connections into physiological indicator to predict participant's performance on three force control tasks, all of which had correlation coefficients greater than 0.7. These results indicate that brain functional network strength can provide a widely applicable biomarker for sustained attention tasks.
尽管注意力在认知和感知科学中占有重要作用，但是缺少一种简单的方法来衡量一个人的注意力能力。我们定义持续注意力任务下的脑功能网络连接的强度作为预测行为表现的生理学指标。本文采集了 14 名被试在三种力控制任务中的行为学和脑电数据。通过力的方差和容差乘积的倒数来计算行为学表现评分。通过小波相干的方法对脑电数据构建网络连接，然后将连接矩阵的每条边和行为学评分进行相关分析。线性回归模型将那些显著相关的网络连接组合成生理学指标来预测被试三种力控制任务的行为表现，相关系数均大于 0.7。这些结果表明脑功能网络的连接强度可以为持续注意力任务提供一个广泛适用的生物标记。.
Keywords: brain functional network; force control task; sustained attention; wavelet coherence.