IEEE Trans Neural Syst Rehabil Eng. 2019 May;27(5):895-904. doi: 10.1109/TNSRE.2019.2905894. Epub 2019 Apr 11.
In brain-computer interfaces (BCIs), the typical models of the EEG observations usually lead to a poor estimation of the trial covariance matrices, given the high non-stationarity of the EEG sources. We propose the application of two techniques that significantly improve the accuracy of these estimations and can be combined with a wide range of motor imagery BCI (MI-BCI) methods. The first one scales the observations in such a way that implicitly normalizes the common temporal strength of the source activities. When the scaling applies independently to the trials of the observations, the procedure justifies and improves the classical preprocessing for the EEG data. In addition, when the scaling is instantaneous and independent for each sample, the procedure particularizes to Tyler's method in statistics for obtaining a distribution-free estimate of scattering. In this case, the proposal provides an original interpretation of this existing method as a technique that pursuits an implicit instantaneous power-normalization of the underlying source processes. The second technique applies to the classifier and improves its performance through a convenient regularization of the features covariance matrix. Experimental tests reveal that a combination of the proposed techniques with the state-of-the-art algorithms for motor-imagery classification provides a significant improvement in the classification results.
在脑机接口 (BCI) 中，由于 EEG 源的高度非平稳性，EEG 观测的典型模型通常导致试次协方差矩阵的估计效果不佳。我们提出了两种技术的应用，这两种技术可以显著提高这些估计的准确性，并可以与广泛的运动想象脑机接口 (MI-BCI) 方法相结合。第一种技术以这样的方式对观测进行缩放，即隐式地对源活动的共同时间强度进行归一化。当缩放独立应用于观测的试次时，该过程为 EEG 数据的经典预处理提供了依据并加以改进。此外，当缩放是瞬时的且对每个样本独立时，该过程就具体化为统计学中的 Tyler 方法，以获得无分布估计的散射。在这种情况下，该方法为现有的方法提供了一个新的解释，即将其作为一种追求潜在源过程的隐式瞬时功率归一化的技术。第二种技术应用于分类器，并通过对特征协方差矩阵进行方便的正则化来提高其性能。实验测试表明，将所提出的技术与运动想象分类的最新算法相结合，可以显著提高分类结果。
