Weighted spatial based geometric scheme as an efficient algorithm for analyzing single-trial EEGS to improve cue-based BCI classification. (August 2017)

• Record Type: Journal Article
• Authors: Alimardani, Fatemeh; Boostani, Reza; Blankertz, Benjamin
• Is Part Of: Neural networks. Volume 92(2017)
• Page Start: 69
• Page End: 76
• Publication Date: 2017-08
• Subjects: Riemannian geometry -- Cue-based Brain computer interface -- Weighting algorithm -- Covariance matrix; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2017.02.014
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: There is a growing interest in analyzing the geometrical behavior of electroencephalogram (EEG) covariance matrix in the context of brain computer interface (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 IIa 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.