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A study on EEG quality in physical movements with Steady-State Visual Evoked Potentials

机译:稳态视觉诱发电位对人体运动中脑电图质量的研究

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In this paper, we investigated the quality of ElectroEncephaloGraphic (EEG) signals during performing physical movements. By using a portable EEG device, the Steady-State Visual Evoked Potential (SSVEP) was recorded on parietal and occipital locations. The SSVEP induced by flickering stimuli was successfully observed in the self-paced mimic walking conditions as well as in the sitting conditions. To see the dependence of temporal and spatial filters on the potential performance of Brain-Computer Interfaces (BCI) we applied the signal processing of Principal Component Analysis and Linear Discriminant Analysis. The pattern recognition performances in inferring the subject's eye gaze directions from the EEG signals could be perfect even in the self-paced mimic walking conditions. It was found that three electrodes on parieto-occipital and occipital locations were essential in order to have perfect performances. From these results, we conclude that the applications using SSVEP-based BCI can be realized even in the physically moving context.
机译:在本文中,我们研究了进行身体运动时脑电图(EEG)信号的质量。通过使用便携式EEG设备,在顶叶和枕叶位置记录了稳态视觉诱发电位(SSVEP)。在自定步调的模拟行走条件下以及在坐姿条件下,都成功地观察到了由闪烁刺激引起的SSVEP。为了了解时间和空间滤波器对脑机接口(BCI)潜在性能的依赖性,我们应用了主成分分析和线性判别分析的信号处理。即使在自定进度的模拟步行条件下,从EEG信号推断对象的视线方向的模式识别性能也可能是完美的。发现在顶枕和枕骨位置上的三个电极对于具有完美的性能是必不可少的。根据这些结果,我们得出结论,即使在物理移动的环境中,也可以实现使用基于SSVEP的BCI的应用程序。

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