Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Manifold-regression to predict from MEG/EEG brain signals without source modeling
Authors: David Sabbagh, Pierre Ablin, Gael Varoquaux, Alexandre Gramfort, Denis A. Engemann
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France. Additional affiliation: Inserm, UMRS-942, Paris Diderot University, Paris, France Additional affiliation: Department of Anaesthesiology and Critical Care, Lariboisière Hospital, Assistance Publique Hôpitaux de Paris, Paris, France |
| Pseudocode | No | The paper does not contain structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code used for data analysis can be found on Git Hub5. |
| Open Datasets | Yes | Finally, we apply these models to the problem of inferring age from brain data [33, 31] on 595 MEG recordings from the Cambridge Center of Aging (Cam-CAN, http://cam-can.org) covering an age range from 18 to 88 years [41]. |
| Dataset Splits | Yes | We measure the score of each method as the average mean absolute error (MAE) obtained with 10-fold cross-validation. |
| Hardware Specification | No | The proposed method, including all data preprocessing, applied on the 500GB of raw MEG data from the Cam-CAN dataset, runs in approximately 12 hours on a regular desktop computer with at least 16GB of RAM. |
| Software Dependencies | No | All numerical experiments were run using the Scikit-Learn software [36], the MNE software for processing M/EEG data [21] and the Py Riemann package [13]. We also ported to Python some part of the Matlab code of Manopt toolbox [9] for computations involving Wasserstein distance. |
| Experiment Setup | Yes | We then used ridge regression and tuned its regularization parameter by generalized cross-validation [20] on a logarithmic grid of 100 values in [10 5, 103] on each training fold of a 10-fold cross-validation loop. |