Multiclass Brain-Computer Interface Classification by Riemannian Geometry
Multiclass Brain-Computer Interface Classification by Riemannian Geometry
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DOI：
10.1109/tbme.2011.2172210
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发表时间：
2012-04-01
影响因子：
4.6
通讯作者：
Jutten, Christian
中科院分区：
文献类型：
--
作者：
Barachant, Alexandre;Bonnet, Stephane;Jutten, Christian
提出了一种新的基于运动想象的脑机接口分类框架。这个框架涉及到协方差矩阵流形中黎曼几何的概念。其主要思想是使用空间协方差矩阵作为EEG信号描述符，并依赖于黎曼几何直接分类这些矩阵使用的对称和正定（SPD）矩阵的流形的拓扑结构。该框架允许提取包含在EEG信号中的空间信息，而不使用空间滤波。提出了两种方法，并与参考方法[多类公共空间模式（CSP）和线性判别分析（LDA）]的多类数据集IIa的BCI竞争IV。第一种方法，称为最小黎曼平均距离（MDRM），是使用黎曼距离和黎曼平均值的最小平均距离（MDM）分类算法的实现。该简单方法显示出与参考方法相当的结果。第二种方法，称为切空间LDA（TSLDA），将协方差矩阵映射到黎曼切空间，其中矩阵可以被向量化并被视为欧几里得对象。然后，应用变量选择过程以降低维度并执行LDA分类。后一种方法优于参考方法，将平均分类准确率从65.1%提高到70.2%。
This paper presents a new classification framework for brain-computer interface (BCI) based on motor imagery. This framework involves the concept of Riemannian geometry in the manifold of covariance matrices. The main idea is to use spatial covariance matrices as EEG signal descriptors and to rely on Riemannian geometry to directly classify these matrices using the topology of the manifold of symmetric and positive definite (SPD) matrices. This framework allows to extract the spatial information contained in EEG signals without using spatial filtering. Two methods are proposed and compared with a reference method [multiclass Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA)] on the multiclass dataset IIa from the BCI Competition IV. The first method, named minimum distance to Riemannian mean (MDRM), is an implementation of the minimum distance to mean (MDM) classification algorithm using Riemannian distance and Riemannian mean. This simple method shows comparable results with the reference method. The second method, named tangent space LDA (TSLDA), maps the covariance matrices onto the Riemannian tangent space where matrices can be vectorized and treated as Euclidean objects. Then, a variable selection procedure is applied in order to decrease dimensionality and a classification by LDA is performed. This latter method outperforms the reference method increasing the mean classification accuracy from 65.1% to 70.2%.