Riemannian Approaches in Brain-Computer Interfaces: A Review

By Florian Yger, Maxime Bérar, Fabien Lotte. IEEE transactions on neural systems and rehabilitation engineering, 2017

390 citations recorded by the source corpus.

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Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2018, methodology): “Recent years have also witnessed an increasing interest in using the EEG covariance matrices for both classification and regression [1], [7], [41], [42].”
Federated Transfer Learning for EEG Signal Classification (2020, methodology): “Furthermore, following the successful Riemannian approach [6], [7], [8], [9], [10], our architecture used signal covariance matrix as an input.”
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, methodology): “Therefore, instead of using the Euclidean distance, called the extrinsic distance, an intrinsic distance is used, which is adapted to the geometry of the manifold, and thus to the manner in which the data have been mapped [47, 232].”
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, positive): “Since the covariance matrices of EEG trials are SPD and lie on a Riemannian manifold instead of in Euclidean space, Riemannian approaches [21] have become popular in EEG-based BCIs.”
Mental State Classification Using Multi-Graph Features (2022, background): “Recent research has also investigated representing EEG signals as matrices [5, 6].”