We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| DavidSabbagh/NeurIPS19_manifold-regression-meeg | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| distance2 | Ran | DavidSabbagh/NeurIPS19_manifold-regression-meeg/library/featuring.py code served (permissive licence) · get_code("972c375be578ae1a") |
| mean_covs | Ran | DavidSabbagh/NeurIPS19_manifold-regression-meeg/library/featuring.py code served (permissive licence) · get_code("4f55e86fef1e6253") |
| to_quotient | Ran | DavidSabbagh/NeurIPS19_manifold-regression-meeg/library/featuring.py code served (permissive licence) · get_code("a0d67ba735278ab0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Magnetoencephalography and electroencephalography (M/EEG) can reveal neuronal dynamics non-invasively in real-time and are therefore appreciated methods in medicine and neuroscience. Recent advances in modeling brain-behavior relationships have highlighted the effectiveness of Riemannian geometry for summarizing the spatially correlated time-series from M/EEG in terms of their covariance. However, after artefact-suppression, M/EEG data is often rank deficient which limits the application of Riemannian concepts. In this article, we focus on the task of regression with rank-reduced covariance matrices. We study two Riemannian approaches that vectorize the M/EEG covariance between-sensors through projection into a tangent space. The Wasserstein distance readily applies to rank-reduced data but lacks affine-invariance. This can be overcome by finding a common subspace in which the covariance matrices are full rank, enabling the affine-invariant geometric distance. We investigated the implications of these two approaches in synthetic generative models, which allowed us to control estimation bias of a linear model for prediction. We show that Wasserstein and geometric distances allow perfect out-of-sample prediction on the generative models. We then evaluated the methods on real data with regard to their effectiveness in predicting age from M/EEG covariance matrices. The findings suggest that the data-driven Riemannian methods outperform different sensor-space estimators and that they get close to the performance of biophysics-driven source-localization model that requires MRI acquisitions and tedious data processing. Our study suggests that the proposed Riemannian methods can serve as fundamental building-blocks for automated large-scale analysis of M/EEG.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1906.02687")
get_code_for_paper("1906.02687")
have("1906.02687")
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