An individualized model that predicts trial-by-trial working memory performance is instrumental for personalized interventions. Here, we propose a single-trial electroencephalography (EEG) classification process predicting individuals' responses, that is, target correct versus target non-correct during a working memory task, N-back. We used event-related (de-)synchronization (ERD and ERS) prior to an anticipatory cue as features. The proposed comprehensive process addresses single-trial EEG classification challenges such as temporal overlap between training and testing datasets, feature selection's stability, and significance of the classification accuracy which have been often overlooked. Our model identified for the first time a few (ranged between 4 and 10) brain regions and oscillations where ERD and ERS predicted an individual's performance. Mean (SD) prediction accuracy across 50 participants (mean age [SD] = 28.56 [7.55]) was 69.51% (8.41). Accuracy was significantly above chance in 34 participants. This machine learning-based approach provides a proof of principle for individualizing EEG targets for potential interventions.

中文翻译：

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通过对脑电图信号进行分类，个性化地实时预测工作记忆性能

预测逐次试验工作记忆表现的个性化模型有助于个性化干预。在这里，我们提出了一种单次试验脑电图 (EEG) 分类过程来预测个体的反应，即在工作记忆任务 N-back 期间目标正确与目标不正确。我们在预期提示之前使用事件相关（去）同步（ERD 和 ERS）作为特征。所提出的综合过程解决了单次试验脑电图分类挑战，例如训练和测试数据集之间的时间重叠、特征选择的稳定性以及经常被忽视的分类准确性的重要性。我们的模型首次确定了 ERD 和 ERS ​​预测个体的几个（范围在 4 到 10 个）大脑区域和振荡。的表现。50 名参与者（平均年龄 [SD] = 28.56 [7.55]）的平均 (SD) 预测准确率为 69.51% (8.41)。34 名参与者的准确率明显高于偶然性。这种基于机器学习的方法为个性化 EEG 目标以进行潜在干预提供了原理证明。