There is a growing interest in analyzing the geometrical behavior of electroencephalogram (EEG) covariance matrix in the context of braincomputerinterface (BCI). The bottleneck of the current Riemannian framework is...
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There is a growing interest in analyzing the geometrical behavior of electroencephalogram (EEG) covariance matrix in the context of braincomputerinterface (BCI). The bottleneck of the current Riemannian framework is the bias of the mean vector of EEG signals to the noisy trials, which deteriorates the covariance matrix in the manifold space. This study presents a spatial weighting scheme to reduce the effect of noisy trials on the mean vector. To assess the proposed method, dataset Ha from BCI competition IV, containing the EEG trials of 9 subjects performing four mental tasks, was utilized. The performance of the proposed method is compared to the classical Riemannian method along with Common Spatial Pattern (CSP) on the dataset. The results show that when considering just two imagery classes, the proposed method performs on par with CSP method, whereas in the multi class scenario, the proposed algorithm outperforms the CSP approach on seven out of nine subjects. Incidentally, the proposed method obtains better accuracy for the majority of subjects compared to the classical Riemannian method. (C) 2017 Elsevier Ltd. All rights reserved.
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