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An EEG-based brain-computer interface for dual task driving detection

机译:基于脑电图的脑机接口,用于双任务驾驶检测

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摘要

The development of brain-computer interfaces (BCI) for multiple applications has undergone extensive growth in recent years. Since distracted driving is a significant cause of traffic accidents, this study proposes one BCI system based on EEC for distracted driving. The removal of artifacts and the selection of useful brain sources are the essential and critical steps in the application of electroencephalography (EEG)-based BCI. In the first model, artifacts are removed, and useful brain sources are selected based on the independent component analysis (ICA). In the second model, all distracted and concentrated EEC epochs are recognized with a self-organizing map (SOM). This BCI system automatically identified independent components with artifacts for removal and detected distracted driving through the specific brain sources which are also selected automatically. The accuracy of the proposed system approached approximately 90% for the recognition of EEC epochs of distracted and concentrated driving according to the selected frontal and left motor components.
机译:近年来,针对多种应用的脑机接口(BCI)的开发经历了广泛的增长。由于分心驾驶是交通事故的重要原因,因此本研究提出了一种基于EEC的BCI系统,用于分心驾驶。在基于脑电图(EEG)的BCI应用中,去除伪像和选择有用的脑源是必不可少的关键步骤。在第一个模型中,去除了伪影,并基于独立成分分析(ICA)选择了有用的脑源。在第二个模型中,所有分心和集中的EEC时期都可以通过自组织图(SOM)进行识别。该BCI系统会自动识别带有伪影的独立组件,以去除这些伪影,并通过特定的脑源检测到分散注意力的驾驶,这些脑源也会自动选择。所建议系统的精度接近90%,可根据所选的前部和左部电机组件识别出分散注意力和集中驾驶的EEC时期。

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