Monitoring working memory load during computer-based tasks with EEG pattern recognition methods
Monitoring working memory load during computer-based tasks with EEG pattern recognition methods
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DOI：
10.1518/001872098779480578
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发表时间：
1998-03-01
期刊：
影响因子：
3.3
通讯作者：
Rush, G
中科院分区：
文献类型：
--
作者：
Gevins, A;Smith, ME;Rush, G
我们评估工作记忆负荷在计算机使用神经网络模式识别应用于脑电频谱特征。8名受试者进行了高、中、低负荷工作记忆任务，随着负荷的增加，额叶theta脑电活动增加，α脑电活动减少。这些变化可能反映了任务难度相关的脑力劳动和分配给任务表现的皮质资源的比例增加。在网络分析中，高负荷和低负荷水平的测试数据段的区别优于95%的准确性。超过80%的与中等负载相关的测试数据段可以与高负载或低负载数据段区分开来。当将用来自一天的数据训练的网络应用于来自另一天的数据时，当将用来自一个任务的数据训练的网络应用于来自另一个任务的数据时，以及当将用来自一组参与者的数据训练的网络应用于来自新参与者的数据时，也实现了统计上显著的分类。这些结果支持了使用基于EEG的方法监测人机交互过程中的认知负荷的可行性。
We assessed working memory load during computer use with neural network pattern recognition applied to EEG spectral features. Eight participants performed high-, moderate-, and low-load working memory tasks, Frontal theta EEG activity increased and alpha activity decreased with increasing load. These changes probably reflect task difficulty-related increases in mental effort and the proportion of cortical resources allocated to task performance. In network analyses, test data segments from high and low load levels were discriminated with better than 95% accuracy. More than 80% of test data segments associated with a moderate load could be discriminated from high- or low-load data segments. Statistically significant classification was also achieved when applying networks trained with data from one day to data from another day, when applying networks trained with data from one task to data from another task, and when applying networks trained with data from a group of participants to data from new participants. These results support the feasibility of using EEG-based methods for monitoring cognitive load during human-computer interaction.