ReviewNeuroinformatics2021
Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI.
Review in Neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- PADA: perturbation-aware differential Riemannian attention for robust motor imagery decoding.Frontiers in neuroscience · 2026Article
- Artificial intelligence-based analysis of visual electrophysiological signals for clinical interpretation support.Frontiers in neuroscience · 2026Review
- Independent Filter Analysis for Group Discrimination in fMRI.bioRxiv : the preprint server for biology · 2025Article
- Riemannian geometry boosts functional near-infrared spectroscopy-based brain-state classification accuracy.Neurophotonics · 2025Article
- Optimizing dance motion reconstruction using a two-dimensional matrix approach with hybrid genetic and fuzzy logic differential evolution.Scientific reports · 2025Article
- GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals.Patterns (New York, N.Y.) · 2025Article
- Hybrid BCI for Meal-Assist Robot Using Dry-Type EEG and Pupillary Light Reflex.Biomimetics (Basel, Switzerland) · 2025Article
- A novel scrolling text reading paradigm for improving the performance of multiclass and hybrid brain computer interface systems.PloS one · 2025Article
- Frequency tagging of spatial attention using periliminal flickers.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Decoding executed and imagined grasping movements from distributed non-motor brain areas using a Riemannian decoder.Frontiers in neuroscience · 2023Article
- Mind wandering state detection during video-based learning via EEG.Frontiers in human neuroscience · 2023Article
- Dual Passive Reactive Brain-Computer Interface: A Novel Approach to Human-Machine Symbiosis.Frontiers in neuroergonomics · 2022Article
- Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans.PloS one · 2022Article
- Principles and open questions in functional brain network reconstruction.Human brain mapping · 2021Review
- Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The firstgeneration of brain-computer interfaces (BCI) classifies multi-channel electroencephalographic (EEG) signals, enhanced by optimized spatial filters.The second generation directly classifies covariance matrices estimated on EEG signals, based on straightforward algorithms such as the minimum-distance-to-Riemannian-mean (MDRM). Classification results vary greatly depending on the chosen Riemannian distance or divergence, whose definitions and reference implementations are spread across a wide mathematical literature. This paper reviews all the Riemannian distances and divergences to process covariance matrices, with an implementation compatible with BCI constraints. The impact of using different metrics is assessed on a steady-state visually evoked potentials (SSVEP) dataset, evaluating centers of classes and classification accuracy. Riemannian approaches embed crucial properties to process EEG data. The Riemannian centers of classes outperform Euclidean ones both in offline and online setups. Some Riemannian distances and divergences have better performances in terms of classification accuracy, while others have appealing computational efficiency.
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Registered trials
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