IEEE Trans Neural Syst Rehabil Eng. 2017 Nov;25(11):2157-2168. doi: 10.1109/TNSRE.2017.2699784. Epub 2017 Apr 28.
Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for electroencephalogram (EEG)-based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30%-8.30%, and increase the estimation correlation coefficient by 6.59%-11.13%.
黎曼几何已成功应用于许多脑机接口 (BCI) 分类问题，并表现出卓越的性能。在本文中，它首次被应用于 BCI 回归问题，这是 BCI 应用的一个重要类别。更具体地说，我们提出了一种新的基于脑电图 (EEG) 的 BCI 回归问题的特征提取方法：首先使用空间滤波器来提高 EEG 试验的信号质量，同时降低协方差矩阵的维数，然后提取黎曼切空间特征。我们在大规模持续注意精神警觉任务中测量的 EEG 信号的反应时间估计中验证了所提出方法的性能，并表明与传统的功率带特征相比，切空间特征可以将均方根估计误差降低 4.30%-8.30%，并将估计相关系数提高 6.59%-11.13%。
