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Spatio-temporal analysis of error-related brain activity in active and passive brain-computer interfaces

机译:活性脑 - 计算机接口中误差脑活动的时空分析

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Electroencephalography (EEG)-based brain-computer interface (BCD systems infer brain signals recorded via EEG without using common neuromuscular pathways. User brain response to BCI error is a contributor to non-stationarity of the EEG signal and poses challenges in developing reliable active BCI control. Many passive BCI implementations, on the other hand, have the detection of error-related brain activity as their primary goal. Therefore, reliable detection of this signal is crucial in both active and passive BCIs. In this work, we propose CREST: a novel covariance-based method that uses Riemannian and Euclidean geometry and combines spatial and temporal aspects of the feedback-related brain activity in response to BCI error. We evaluate our proposed method with two datasets: an active BCI for 1-D cursor control using motor imagery and a passive BCI for 2-D cursor control. We show significant improvement across participants in both datasets compared to existing methods.
机译:基于脑电图(EEG)的脑电脑接口(BCD系统推断通过EEG记录的脑信号而不使用常见的神经肌肉途径。对BCI错误的用户脑响应是EEG信号的非实用性的贡献者,并且在开发可靠的Active BCI中提出挑战另一方面,许多被动BCI实现具有误差相关的大脑活动作为其主要目标。因此,对该信号的可靠性检测在主动和被动BCIS中是至关重要的。在这项工作中,我们提出了Crest:一种基于协方差的基于协方差的方法,使用riemannian和欧几里德几何形状,并结合反馈相关脑活动的空间和时间方面响应BCI误差。我们用两个数据集评估我们提出的方法:使用的Active BCI用于1-D光标控制使用用于2-D光标控制的电动机图像和无源BCI。与现有方法相比,我们对参与者的参与者显着改进。

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