Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review

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Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces (2018, methodology): “In [2], [12], [15] a comparison of Riemannian techniques to other standard methods, like Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA), is presented.”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “Riemannian geometry has been successfully utilized in many BCI classiﬁcation problems (Kalunga et al., 2016; Congedo et al., 2017; Wu et al., 2017; Yger et al., 2017; Gaur et al., 2018; Guan et al., 2019; Han et al., 2019; Majidov and Whangbo, 2019 ) and has demonstrated superior performance.”
Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review (2024, methodology): “Under these conditions, all information on the Riemannian manifold at a given point can be invariantly projected onto the tangent space (Euclidean space) using a logarithmic map [154,155].”
Mental State Classification Using Multi-Graph Features (2022, background): “Recent research has also investigated representing EEG signals as matrices [5, 6].”