Larzabal Christelle, Auboiroux Vincent, Karakas Serpil, Charvet Guillaume, Benabid Alim-Louis, Chabardes Stephan, Costecalde Thomas, Bonnet Stéphane
University Grenoble Alpes, CEA, LETI, Clinatec, F-38000 Grenoble, France.
CHU Grenoble Alpes, Grenoble, France.
J Neural Eng. 2021 Apr 8;18(5). doi: 10.1088/1741-2552/abf291.
. Over the last decade, Riemannian geometry has shown promising results for motor imagery classification. However, extracting the underlying spatial features is not as straightforward as for applying common spatial pattern (CSP) filtering prior to classification. In this article, we propose a simple way to extract the spatial patterns obtained from Riemannian classification: the Riemannian spatial pattern (RSP) method, which is based on the backward channel selection procedure.. The RSP method was compared to the CSP approach on ECoG data obtained from a quadriplegic patient while performing imagined movements of arm articulations and fingers.Similar results were found between the RSP and CSP methods for mapping each motor imagery task with activations following the classical somatotopic organization. Clustering obtained by pairwise comparisons of imagined motor movements however, revealed higher differentiation for the RSP method compared to the CSP approach. Importantly, the RSP approach could provide a precise comparison of the imagined finger flexions which added supplementary information to the mapping results.Our new RSP method illustrates the interest of the Riemannian framework in the spatial domain and as such offers new avenues for the neuroimaging community. This study is part of an ongoing clinical trial registered with ClinicalTrials.gov, NCT02550522.
在过去十年中，黎曼几何在运动想象分类方面显示出了有前景的结果。然而，提取潜在的空间特征并不像在分类之前应用共同空间模式（CSP）滤波那样直接。在本文中，我们提出了一种简单的方法来提取从黎曼分类中获得的空间模式：黎曼空间模式（RSP）方法，该方法基于反向通道选择程序。将RSP方法与从一名四肢瘫痪患者在进行手臂关节和手指的想象运动时获得的脑皮层电图（ECoG）数据上的CSP方法进行了比较。在将每个运动想象任务与遵循经典躯体定位组织的激活进行映射时，RSP和CSP方法之间发现了相似的结果。然而，通过对想象运动的成对比较获得的聚类显示，与CSP方法相比，RSP方法具有更高的区分度。重要的是，RSP方法可以对想象的手指屈曲进行精确比较，这为映射结果增添了补充信息。我们新的RSP方法说明了黎曼框架在空间领域的价值，因此为神经成像界提供了新的途径。本研究是一项正在进行的临床试验的一部分，该试验已在ClinicalTrials.gov上注册，注册号为NCT02550522。
