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.

翻译：执行功能是一种使人类能够以目标导向的方式规划、组织和调节自身行为的认知过程。目前文献中尚未探讨如何理解与分类纵向干预（如经颅直流电刺激（tDCS））后执行功能的改变。本研究采用功能连接与机器学习算法对tDCS后执行功能表现进行分类。将50名受试者分为实验组和安慰剂对照组。在第1天收集受试者执行执行功能任务时的脑电图（EEG）数据。实验组在第2至第8天任务训练期间接受tDCS，而对照组接受伪tDCS。第10天，受试者重复第1天规定的任务。从EEG数据中提取不同的功能连接指标，最终用于通过多种机器学习算法对执行功能表现进行分类。结果显示，偏定向相干性与多层感知器（结合递归特征消除）的新颖组合达到了95.44%的高分类准确率。我们讨论了该结果对开发用于评估和增强tDCS后执行功能表现的实时神经反馈系统的意义。