This paper proposes a strategy to handle missing data for the classification of electroencephalograms using covariance matrices. It relies on the observed-data likelihood within an expectation-maximization algorithm. This approach is compared to two existing state-of-the-art methods: (i) covariance matrices computed with imputed data; (ii) Riemannian averages of partially observed covariance matrix. All approaches are combined with the minimum distance to Riemannian mean classifier and applied to a classification task of two widely known paradigms of brain-computer interfaces. In addition to be applicable for a wider range of missing data scenarios, the proposed strategy generally performs better than other methods on the considered real EEG data.

翻译：本文提出利用共变矩阵处理电子脑图分类缺失数据的战略,依靠预期最大化算法中观察到的数据可能性,与现有的两种最新方法进行比较:(一) 与估算数据计算的共变矩阵;(二) 部分观察到的共变矩阵的里曼尼平均平均值。所有方法都与里曼平均分级器的最低距离相结合,并适用于两种广为人知的大脑计算机界面模式的分类任务。除了适用于更广泛的缺失数据假设情景外,拟议战略在被视为真实的EEG数据上通常比其他方法效果更好。