Covert attention has been repeatedly shown to impact on EEG responses after single and repeated practice sessions. Machine learning techniques are increasingly adopted to classify single-trial EEG responses thereby primarily relying on amplitude-based features instead of latency-based features. In this study, we investigated changes in EEG response signatures of nine healthy older subjects when performing 10 sessions of covert attention training. We show that, when we trained classifiers to distinguish recorded EEG patterns between the two experimental conditions (a target stimulus is “present” or “not present”), latency-based classifiers outperform the amplitude-based ones and that classification accuracy improved along with behavioral accuracy, providing supportive evidence of brain plasticity.

中文翻译：

---

使用延迟特征分类的单次试验脑电图反应

在单次和重复练习后，隐蔽注意力已被反复证明会影响脑电图反应。机器学习技术越来越多地用于对单次试验 EEG 响应进行分类，因此主要依赖于基于幅度的特征而不是基于延迟的特征。在这项研究中，我们调查了 9 名健康老年受试者在进行 10 次隐蔽注意力训练时脑电图反应特征的变化。我们表明，当我们训练分类器以区分两种实验条件（目标刺激是“存在”或“不存在”）之间记录的脑电图模式时，基于延迟的分类器优于基于幅度的分类器，并且分类精度随着提高行为准确性，为大脑可塑性提供支持性证据。