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..
A state-space model of cross-region dynamic connectivity in MEG/EEG
Authors: Ying Yang, Elissa Aminoff, Michael Tarr, Robert E. Kass
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared with a two-step method, which first obtains the commonly used minimum-norm estimates of source activity, and then fits the auto-regressive model, our state-space model yielded smaller estimation errors on simulated data where the model assumptions held. When applied on empirical MEG data from one participant in a scene-processing experiment, our state-space model also demonstrated intriguing preliminary results |
| Researcher Affiliation | Academia | Ying Yang Elissa M. Aminoff Michael J. Tarr Robert E. Kass Carnegie Mellon University, Fordham University |
| Pseudocode | No | The paper describes the EM algorithm using mathematical formulas and textual descriptions but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation of the model and the E-M algorithm in Python is available at github.com/Ying Yang/MEEG_connectivity. |
| Open Datasets | No | We simulated MEG sensor data according to our model assumptions. The 306-channel MEG data were recorded while a human participant was viewing 362 photos of various scenes. |
| Dataset Splits | No | Each simulation had q = 200 trials, and 5 independent simulations for each a value were generated. Given the data, we estimated the dynamic connectivity between the neural responses to the 362 images in the two ROIs (EVC and PPA), using our state-space model and the two-step MNE method. We also bootstrapped the 362 observations 27 times to obtain standard deviations of entries. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The implementation of the model and the E-M algorithm in Python is available at github.com/Ying Yang/MEEG_connectivity. (Only specifies 'Python' without a version or any specific library versions). |
| Experiment Setup | Yes | In the prior of {At}T t=1, we set λ0 = 0 and λ1 = 0.1. ... Each simulation had q = 200 trials, and 5 independent simulations for each a value were generated. ... In the prior of {At}T t=1, we set λ0 = 0 and λ1 = 1.0; ... The data was down-sampled from a sampling rate of 1 k Hz to 100 Hz, and cropped within 100 700 ms, where 0 ms marked the stimulus onset. Together, we had T + 1 = 80 time points |