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 | Conference PDF | Archive PDF | Plain Text | 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. |