Estimating workload using EEG spectral power and ERPs in the n-back task
Estimating workload using EEG spectral power and ERPs in the n-back task
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DOI：
10.1088/1741-2560/9/4/045008
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发表时间：
2012-08-01
影响因子：
4
通讯作者：
Oostenveld, Robert
中科院分区：
文献类型：
--
作者：
Brouwer, Anne-Marie;Hogervorst, Maarten A.;Oostenveld, Robert
以往的研究表明，脑电图（EEG）频谱功率（特别是α和θ波段）和事件相关电位（ERP）（特别是P300）都可以用来衡量脑力劳动或记忆负荷。我们比较他们的能力，估计在一个良好的控制任务的工作量水平。此外，我们联合收割机在一个单一的分类模型，以检查这是否会导致更高的分类精度比任何一个单独的措施。参与者观看一系列视觉呈现的字母，并指出当前字母是否与前一个字母（n个实例）相同。不同的n值会导致n值的变化。我们开发了不同的分类模型，使用ERP功能，频率功率功能或组合（融合）。模型的训练和测试模拟了在线工作负载估计情况。我们所有的ERP，电源和融合模型提供的分类精度在80%和90%之间区分最高和最低的工作负荷条件下2分钟后。对于32的35名参与者，分类是显着高于机会水平后2.5秒（或一个字母）的融合模型估计。模型之间的差异是相当小的，虽然融合模型的性能优于其他模型时，只有短的数据段可用于估计工作量。
Previous studies indicate that both electroencephalogram (EEG) spectral power (in particular the alpha and theta band) and event-related potentials (ERPs) (in particular the P300) can be used as a measure of mental work or memory load. We compare their ability to estimate workload level in a well-controlled task. In addition, we combine both types of measures in a single classification model to examine whether this results in higher classification accuracy than either one alone. Participants watched a sequence of visually presented letters and indicated whether or not the current letter was the same as the one (n instances) before. Workload was varied by varying n. We developed different classification models using ERP features, frequency power features or a combination (fusion). Training and testing of the models simulated an online workload estimation situation. All our ERP, power and fusion models provide classification accuracies between 80% and 90% when distinguishing between the highest and the lowest workload condition after 2 min. For 32 out of 35 participants, classification was significantly higher than chance level after 2.5 s (or one letter) as estimated by the fusion model. Differences between the models are rather small, though the fusion model performs better than the other models when only short data segments are available for estimating workload.