Rodrigues Pedro L C, Congedo Marco, Jutten Christian
IEEE Trans Biomed Eng. 2021 Feb;68(2):673-684. doi: 10.1109/TBME.2020.3010854. Epub 2021 Jan 20.
We present a transfer learning method for datasets with different dimensionalities, coming from different experimental setups but representing the same physical phenomena. We focus on the case where the data points are symmetric positive definite (SPD) matrices describing the statistical behavior of EEG-based brain computer interfaces (BCI).
Our proposal uses a two-step procedure that transforms the data points so that they become matched in terms of dimensionality and statistical distribution. In the dimensionality matching step, we use isometric transformations to map each dataset into a common space without changing their geometric structures. The statistical matching is done using a domain adaptation technique adapted for the intrinsic geometry of the space where the datasets are defined.
We illustrate our proposal on time series obtained from BCI systems with different experimental setups (e.g., different number of electrodes, different placement of electrodes). The results show that the proposed method can be used to transfer discriminative information between BCI recordings that, in principle, would be incompatible.
Such findings pave the way to a new generation of BCI systems capable of reusing information and learning from several sources of data despite differences in their electrodes positioning.
我们提出一种迁移学习方法，用于处理来自不同实验设置但代表相同物理现象的具有不同维度的数据集。我们重点关注数据点为对称正定（SPD）矩阵的情况，这些矩阵描述了基于脑电图的脑机接口（BCI）的统计行为。
我们的提议采用两步程序来转换数据点，使其在维度和统计分布方面相互匹配。在维度匹配步骤中，我们使用等距变换将每个数据集映射到一个公共空间，而不改变其几何结构。统计匹配则使用一种适用于定义数据集的空间的内在几何的域适应技术来完成。
我们在从具有不同实验设置（例如，不同电极数量、不同电极放置）的BCI系统获得的时间序列上展示了我们的提议。结果表明，所提出的方法可用于在原则上不兼容的BCI记录之间传递判别信息。
这些发现为新一代BCI系统铺平了道路，这类系统能够重用信息并从多个数据源学习，尽管它们的电极定位存在差异。
