We investigated whether a passive brain–computer interface that was trained to distinguish low and high mental workload in the electroencephalogram (EEG) can be used to identify (1) texts of different readability difficulties and (2) texts read at different presentation speeds. For twelve subjects we calibrated a subject-dependent, but task-independent predictive model classifying mental workload. We then recorded EEG data from each subject, while twelve texts in blocks of three were presented to them word by word. Half of the texts were easy, and the other half were difficult texts according to classic reading formulas. From each text category three texts were read at a self-adjusted comfortable presentation speed and the other three at an increased speed. For each subject we applied the predictive model to EEG data of each word of the twelve texts. We found that the resulting predictive values for mental workload were higher for difficult texts than for easy texts. Predictive values from texts presented at an increased speed were also higher than for those presented at a normal self-adjusted speed. The results suggest that the task-independent predictive model can be used on single-subject level to build a highly predictive user model of the reader over time. Such a model could be employed in a system which continuously monitors brain activity related to mental workload and adapts to specific reader’s abilities and characteristics by adjusting the difficulty of text materials and the way it is presented to the reader in real time. A neuroadaptive system like this could foster efficient reading and text-based learning by keeping readers’ mental workload levels at an individually optimal level.

中文翻译：

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面向改善数字阅读的神经适应性支持技术：基于被动 BCI 的对阅读过程中文本难度和呈现速度施加的心理负荷的评估

我们研究了一种被动脑机接口，该接口经过训练以区分脑电图 (EEG) 中的低和高脑力负荷，是否可用于识别 (1) 不同可读性困难的文本和 (2) 以不同呈现速度阅读的文本。对于 12 个科目，我们校准了一个与科目相关但与任务无关的预测模型，用于对心理工作量进行分类。然后我们记录了每个受试者的 EEG 数据，同时将 12 个文本以三个块为一组逐字呈现给他们。根据经典阅读公式，一半的文本是简单的，另一半是困难的文本。从每个文本类别中，以自我调整的舒适呈现速度阅读三篇文本，并以增加的速度阅读其他三篇。对于每个主题，我们将预测模型应用于十二个文本中每个单词的 EEG 数据。我们发现困难文本的心理工作量预测值高于简单文本。以增加的速度呈现的文本的预测值也高于以正常的自我调整速度呈现的文本的预测值。结果表明，独立于任务的预测模型可用于单主题级别，以随着时间的推移构建具有高度预测性的读者用户模型。这样的模型可用于持续监测与脑力负荷相关的大脑活动并通过调整文本材料的难度和实时呈现给读者的方式来适应特定读者的能力和特征的系统中。