Predicting and understanding the changes in cognitive performance, especially after a longitudinal intervention, is a fundamental goal in neuroscience. Longitudinal brain stimulation-based interventions like transcranial direct current stimulation (tDCS) induce short-term changes in the resting membrane potential and influence cognitive processes. However, very little research has been conducted on predicting these changes in cognitive performance post-intervention. In this research, we intend to address this gap in the literature by employing different EEG-based functional connectivity analyses and machine learning algorithms to predict changes in cognitive performance in a complex multitasking task. Forty subjects were divided into experimental and active-control conditions. On Day 1, all subjects executed a multitasking task with simultaneous 32-channel EEG being acquired. From Day 2 to Day 7, subjects in the experimental condition undertook 15 minutes of 2mA anodal tDCS stimulation during task training. Subjects in the active-control condition undertook 15 minutes of sham stimulation during task training. On Day 10, all subjects again executed the multitasking task with EEG acquisition. Source-level functional connectivity metrics, namely phase lag index and directed transfer function, were extracted from the EEG data on Day 1 and Day 10. Various machine learning models were employed to predict changes in cognitive performance. Results revealed that the multi-layer perceptron and directed transfer function recorded a cross-validation training RMSE of 5.11% and a test RMSE of 4.97%. We discuss the implications of our results in developing real-time cognitive state assessors for accurately predicting cognitive performance in dynamic and complex tasks post-tDCS intervention

翻译：预测和理解认知绩效的变化，特别是在纵向干预后的变化，是神经科学的基本目标。基于纵向脑刺激的干预措施（如经颅直流电刺激，tDCS）会诱导静息膜电位的短期改变，并影响认知过程。然而，关于预测干预后认知绩效变化的研究甚少。本研究旨在通过运用基于EEG的功能连接分析和机器学习算法，弥补这一文献空白，以预测复杂多任务中认知绩效的变化。40名受试者被分为实验组和主动对照组。第1天，所有受试者在执行多任务的同时采集32通道EEG数据。第2天至第7天，实验组受试者在任务训练期间接受15分钟2mA阳极tDCS刺激；主动对照组受试者在任务训练期间接受15分钟假刺激。第10天，所有受试者再次在执行多任务的同时采集EEG数据。从第1天和第10天的EEG数据中提取源级功能连接指标，即相位延迟指数和定向传递函数。采用多种机器学习模型预测认知绩效的变化。结果显示，多层感知器结合定向传递函数得到的交叉验证训练均方根误差为5.11%，测试均方根误差为4.97%。我们讨论了这些结果对开发实时认知状态评估器以准确预测tDCS干预后动态复杂任务中认知绩效的意义。