Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy
Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy
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DOI：
10.1080/2326263x.2017.1304020
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发表时间：
2017-01-01
影响因子：
2.1
通讯作者：
Shewokis, Patricia A.
中科院分区：
文献类型：
--
作者：
Liu, Yichuan;Ayaz, Hasan;Shewokis, Patricia A.
脑-机接口可以测量操作员的心理负荷水平，在人机交互（HCI）中具有减少人为错误和提高工作效率的应用。在这项研究中，同时记录脑电图（EEG）和功能近红外光谱（fNIRS）相结合的决策融合阶段的分类的3个脑力负荷水平的n-back工作记忆任务。在13名参与者中，单独使用fNIR，单独使用EEG和EEG-fNIRS组合方法的平均三级分类准确率分别为42%，43%和49%。目前的研究证明了一种基于多模态的方法来解码人类的心理工作负荷水平，可能会用于自适应HCI应用程序。
A brain-computer interface that measures the mental workload level of operators has applications in human-computer interactions (HCI) for reducing human error and improving work efficiency. In this study, concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were combined at the decision fusion stage for the classification of three mental workload levels induced by an n-back working-memory task. An average three-class classification accuracy of 42, 43, and 49% has been achieved across 13 participants for the fNIR-alone, EEG-alone, and EEG-fNIRS combined approach, respectively. The current study demonstrated a multimodality-based approach to decode human mental workload levels that may potentially be used for adaptive HCI applications.