Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns.
Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns.
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DOI：
10.3389/fnins.2013.00149
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发表时间：
2013
影响因子：
4.3
通讯作者：
de Jong R
中科院分区：
文献类型：
--
作者：
Dijksterhuis C;de Waard D;Brookhuis KA;Mulder BL;de Jong R
被动脑机接口（BCI）是一种响应用户自发产生的大脑活动的系统，可用于开发交互式任务支持。可以从基于大脑的任务支持中受益的人机系统是驾驶员-汽车交互系统。为了研究这种系统检测视觉运动工作负荷变化的可行性，34 名驾驶员在驾驶模拟器中接受了不同级别的驾驶需求。驾驶需求是通过改变驾驶速度并要求驾驶员遵守单独设定的车道保持性能目标来控制的。在离线分类研究期间，通过将通用空间模式 (CSP) 和费舍尔线性判别分析应用于频率滤波脑电图 (EEG) 数据，对各个驾驶员工作负载水平的差异进行分类。探索了几个频率范围、脑电图上限配置和条件对。研究发现，基于高频、较大电极组和额电极的分类最为准确。根据这些因素，参与者的分类准确率平均达到 95% 左右。高精度和高频率之间的关联表明部分潜在信息并非直接源自神经元活动。尽管如此，从可能反映神经元活动的较低脑电图范围获得了高达 75-80% 的平均分类准确率。对于系统设计人员来说，这意味着无源 BCI 系统可以使用多个频率范围进行工作负载分类。
A passive Brain Computer Interface (BCI) is a system that responds to the spontaneously produced brain activity of its user and could be used to develop interactive task support. A human-machine system that could benefit from brain-based task support is the driver-car interaction system. To investigate the feasibility of such a system to detect changes in visuomotor workload, 34 drivers were exposed to several levels of driving demand in a driving simulator. Driving demand was manipulated by varying driving speed and by asking the drivers to comply to individually set lane keeping performance targets. Differences in the individual driver's workload levels were classified by applying the Common Spatial Pattern (CSP) and Fisher's linear discriminant analysis to frequency filtered electroencephalogram (EEG) data during an off line classification study. Several frequency ranges, EEG cap configurations, and condition pairs were explored. It was found that classifications were most accurate when based on high frequencies, larger electrode sets, and the frontal electrodes. Depending on these factors, classification accuracies across participants reached about 95% on average. The association between high accuracies and high frequencies suggests that part of the underlying information did not originate directly from neuronal activity. Nonetheless, average classification accuracies up to 75–80% were obtained from the lower EEG ranges that are likely to reflect neuronal activity. For a system designer, this implies that a passive BCI system may use several frequency ranges for workload classifications.
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影响因子：
4
作者：
Brouwer, Anne-Marie;Hogervorst, Maarten A.;Oostenveld, Robert
通讯作者：
Oostenveld, Robert
影响因子：
5.9
作者：
Lewis-Evans, Ben;de Waard, Dick;Brookhuis, Karel A.
通讯作者：
Brookhuis, Karel A.
DOI：
10.1109/21.17382
发表时间：
1988-07-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
--
作者：
HANCOCK, PA;CHIGNELL, MH
通讯作者：
CHIGNELL, MH
影响因子：
5.9
作者：
Brookhuis, Karel A.;de Waard, Dick
通讯作者：
de Waard, Dick
影响因子：
5.9
作者：
Bella, Francesco
通讯作者：
Bella, Francesco