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A tensorial approach to single trial recognition for Brain Computer Interface

机译:一种张量方法进行脑计算机接口的单次试验识别

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In this paper, we propose a tensorial approach to single trial recognition in a EEG-based BCI system related to movement related potentials. In this approach input data are considered as tensors instead of more conventional vector or matrix representations. Feature extraction for multiway EEG spectral tensors is solved by using tensor (multi-array) decompositions. For the same EEG motor imagery dataset, the developed algorithms improved the accuracy of classification by almost 10% compared with the common spatial pattern (CSP) method.
机译:在本文中,我们提出了一种基于张量法的基于EEG的BCI系统中与运动相关电位相关的单项试验识别。在这种方法中,输入数据被视为张量,而不是更常规的矢量或矩阵表示。通过使用张量(多阵列)分解解决了多向EEG频谱张量的特征提取。对于相同的EEG运动图像数据集,与常见的空间模式(CSP)方法相比,开发的算法将分类的准确性提高了近10%。

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