Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention.

翻译：注意力是大脑选择性处理特定刺激并过滤无关信息的机制。长期干预（如经颅直流电刺激（tDCS））后注意力变化的特征描述在文献中鲜有强调。为对tDCS后的注意力表现进行分类，本研究采用功能连接与机器学习算法。50名被试被分为实验组与对照组。第1天，被试在执行注意力任务时获取脑电图（EEG）数据。第2天至第8天，实验组接受1mA tDCS刺激，对照组接受假tDCS刺激。第10天，被试重复第1天的任务。利用功能连接指标，通过多种机器学习方法对注意力表现进行分类。结果显示，Adaboost模型结合递归特征消除的分类准确率达91.84%。我们讨论了本研究结果在开发用于评估注意力的神经反馈框架方面的意义。