{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"pyRiemann provides scikit-learn-compatible machine learning algorithms for classifying multivariate data using Riemannian geometry of symmetric positive definite matrices, with support for both NumPy and PyTorch backends.","skillfed_tags":["riemannian-geometry","brain-computer-interface","biosignal-processing"],"use_cases":["Build BCI systems that classify motor imagery or event-related potentials from multichannel EEG recordings","Process hyperspectral remote sensing imagery by estimating and classifying covariance matrices over spatial regions","Perform transfer learning across BCI recording sessions or subjects using Riemannian geometry","Classify synthetic-aperture radar (SAR) images using Hermitian positive definite matrix geometry","Pipeline covariance estimation with tangent-space projection and standard classifiers"],"what_it_does":"pyRiemann is a machine learning library that applies Riemannian geometry to classify multivariate data, particularly biosignals like EEG, MEG, and EMG. It estimates covariance matrices from multichannel time series and uses the geometric properties of symmetric positive definite (SPD) matrices to build classifiers. The package follows scikit-learn conventions, making it easy to integrate into standard machine learning pipelines and workflows.\n\nThe library was designed around brain-computer interface (BCI) applications, supporting motor imagery, event-related potentials, and steady-state visually evoked potentials paradigms. It also handles remote sensing tasks involving hyperspectral and synthetic-aperture radar imagery. Core utilities support both NumPy and PyTorch backends transparently through the Python Array API, enabling optional GPU acceleration when PyTorch tensors are used. Transfer learning between sessions or subjects is supported through extended labels.","worth_installing":"Yes. Active maintenance, low install friction, permissive license, and no known vulnerabilities make it safe to adopt. The scikit-learn-compatible API and support for both NumPy and PyTorch backends provide flexibility. Install if you work with multivariate biosignals, BCI systems, or remote sensing imagery requiring Riemannian geometric classification."},"id":"pyriemann","links":{"html":"https://skillfed.io/packages/pyriemann","md":"https://skillfed.io/packages/pyriemann.md","pypi":"https://pypi.org/project/pyriemann/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-01","license_spdx":null,"license_treatment":"permissive","name":"pyriemann","python_support":"supports_current","summary":"Machine learning for multivariate data with Riemannian geometry"},"popularity":{"monthly_downloads":89512,"position":13654,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.12"}