The common spatial pattern (CSP) approach is known as one of the most popular spatial filtering techniques for EEG classification in motor imagery (MI) based brain-computer interfaces (BCIs). However, it still suffers some drawbacks such as sensitivity to noise, non-stationarity, and limitation to binary classification.Therefore, we propose a novel spatial filtering framework called scaCSP based on the scatter matrices of spatial covariances of EEG signals, which works generally in both binary and multi-class problems whereas CSP can be cast into our framework as a special case when only the range space of the between-class scatter matrix is used in binary cases.We further propose subspace enhanced scaCSP algorithms which easily permit incorporating more discriminative information contained in other range spaces and null spaces of the between-class and within-class scatter matrices in two scenarios: a nullspace components reduction scenario and an additional spatial filter learning scenario.The proposed algorithms are evaluated on two data sets including 4 MI tasks. The classification performance is compared against state-of-the-art competing algorithms: CSP, Tikhonov regularized CSP (TRCSP), stationary CSP (sCSP) and stationary TRCSP (sTRCSP) in the binary problems whilst multi-class extensions of CSP based on pair-wise and one-versus-rest techniques in the multi-class problems. The results show that the proposed framework outperforms all the competing algorithms in terms of average classification accuracy and computational efficiency in both binary and multi-class problems.The proposed scsCSP works as a unified framework for general multi-class problems and is promising for improving the performance of MI-BCIs.

翻译：公共空间模式（CSP）方法是基于运动想象（MI）的脑机接口（BCI）中脑电图（EEG）分类最流行的空间滤波技术之一。然而，它仍然存在一些缺点，例如对噪声敏感、非平稳性以及局限于二分类问题。因此，我们提出了一种新颖的空间滤波框架，称为scaCSP，它基于EEG信号空间协方差的散度矩阵，可一般性地适用于二分类和多分类问题，而CSP可以作为我们框架的一个特例，当仅使用二分类情况下类间散度矩阵的列空间时。我们进一步提出了子空间增强的scaCSP算法，该算法在两种场景下（零空间成分减少场景和额外空间滤波学习场景）能够轻松地纳入包含在类间和类内散度矩阵的其他列空间和零空间中的更多判别信息。所提出的算法在两个包含4个MI任务的数据集上进行了评估。其分类性能与最先进的竞争算法进行了比较：在二分类问题中与CSP、Tikhonov正则化CSP（TRCSP）、平稳CSP（sCSP）和稳定TRCSP（sTRCSP）比较；在多分类问题中与基于成对和一对其余技术的CSP多分类扩展算法比较。结果表明，所提出的框架在二分类和多分类问题的平均分类准确率和计算效率方面均优于所有竞争算法。所提出的scaCSP作为一个针对一般多分类问题的统一框架，有望提高MI-BCI的性能。