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首页> 外文会议>International Conference on BioMedical Engineering and Informatics >Effective user training for motor imagery based brain computer interface with object-directed 3D visual display
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Effective user training for motor imagery based brain computer interface with object-directed 3D visual display

机译：基于电动机的脑电脑接口的有效用户培训与对象定向的3D视觉显示

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Effective user training could help us to improve the discrimination performance of our intention in brain computer interface (BCI). This paper aims to differentiate users left or right hand motor imagery (MI) tasks with different scenarios in 3D virtual environment, as non-object-directed (NOD) scenario, static-object-directed (SOD) scenario and dynamic-object-directed (DOD) scenario respectively. The results have significant differences by applying these three scenarios. Both SOD and DOD scenarios provide better classification accuracy, shorten single-trial period, and need smaller training samples comparing with the NOD case. We conclude that improving visual display may facilitate learning to use a BCI. Further comparing these results between single-subject and multiple-subject paradigm of BCI, we verify better classification performance could also be achieved by the multiple-subject paradigm. We believe these findings have the potential to improve discrimination performance of users intention for EEG-based BCI applications.
机译：有效的用户培训可以帮助我们提高我们在大脑电脑界面（BCI）中的歧视性能。本文旨在区分用户在3D虚拟环境中具有不同方案的左手或右手电机图像（MI）任务，作为非对象导向（NOD）方案，静态对象（SOD）方案和动态对象定向（DOD）分别是场景。结果通过应用这三种情况具有显着差异。 SOD和DOD方案都提供了更好的分类准确性，缩短了单试，并且需要与NOD案例相比的较小培训样本。我们得出结论，改进的视觉显示可能有助于学习使用BCI。进一步将这些结果与BCI的单亲和多个主题范例之间进行了进一步比较，我们验证了更好的分类性能，也可以通过多主题范式实现。我们认为，这些调查结果有可能提高用户意图的攻击基于EEG的BCI应用程序的潜在歧视性能。

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