Executive functioning is a cognitive process that enables humans to plan,
organize, and regulate their behavior in a goal-directed manner. Understanding
and classifying the changes in executive functioning after longitudinal
interventions (like transcranial direct current stimulation (tDCS)) has not
been explored in the literature. This study employs functional connectivity and
machine learning algorithms to classify executive functioning performance
post-tDCS. Fifty subjects were divided into experimental and placebo control
groups. EEG data was collected while subjects performed an executive
functioning task on Day 1. The experimental group received tDCS during task
training from Day 2 to Day 8, while the control group received sham tDCS. On
Day 10, subjects repeated the tasks specified on Day 1. Different functional
connectivity metrics were extracted from EEG data and eventually used for
classifying executive functioning performance using different machine learning
algorithms. Results revealed that a novel combination of partial directed
coherence and multi-layer perceptron (along with recursive feature elimination)
resulted in a high classification accuracy of 95.44%. We discuss the
implications of our results in developing real-time neurofeedback systems for
assessing and enhancing executive functioning performance post-tDCS
administration.

通过功能连接和机器学习算法，研究了经颅直流电刺激（tDCS）后执行功能的变化，并且提出了一种高分类准确率的方法，将部分定向相干和多层感知机相结合，以及递归特征消除。结果表明，这种方法可用于实时神经反馈系统，用于评估和提升经 tDCS 治疗后的执行功能表现。

经功能连接性和机器学习方法分类长期经皮颅电刺激后的执行功能表现

Classification of executive functioning performance post-longitudinal  tDCS using functional connectivity and machine learning methods

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。