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

在这项研究中，我们通过使用不同的基于 EEG 的功能连接分析和机器学习算法，来预测复杂多任务任务中认知表现的变化。结果显示，多层感知器和有向传递函数具有较高的预测准确率。