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Continuous decoding of cognitive load from electroencephalography reveals task-general and task-specific correlates.
Journal of Neural Engineering ( IF 4 ) Pub Date : 2020-10-15 , DOI: 10.1088/1741-2552/abb9bc
Matthew J Boring 1, 2 , Karl Ridgeway 1 , Michael Shvartsman 1 , Tanya R Jonker 1
Affiliation  

Objective . Algorithms to detect changes in cognitive load using non-invasive biosensors (e.g. electroencephalography (EEG)) have the potential to improve human–computer interactions by adapting systems to an individual’s current information processing capacity, which may enhance performance and mitigate costly errors. However, for algorithms to provide maximal utility, they must be able to detect load across a variety of tasks and contexts. The current study aimed to build models that capture task-general EEG correlates of cognitive load, which would allow for load detection across variable task contexts. Approach . Sliding-window support vector machines (SVM) were trained to predict periods of high versus low cognitive load across three cognitively and perceptually distinct tasks: n-back, mental arithmetic, and multi-object tracking. To determine how well these SVMs could generalize to novel tasks, they were trained on data from two of the three tasks and evaluate...

中文翻译:

脑电图认知负荷的连续解码揭示了一般任务和特定任务的相关性。

客观的 。使用非侵入性生物传感器(例如脑电图(EEG))检测认知负荷变化的算法有可能通过使系统适应个人当前的信息处理能力来改善人机交互,这可能会提高性能并减少代价高昂的错误。然而,为了提供最大效用的算法,它们必须能够跨各种任务和上下文检测负载。目前的研究旨在建立模型来捕捉认知负荷的一般任务脑电图相关性,这将允许跨可变任务上下文的负荷检测。方法 。滑动窗口支持向量机 (SVM) 被训练来预测三个认知和感知不同任务的高认知负荷和低认知负荷周期:n-back、心算和多对象跟踪。
更新日期:2020-10-19
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