A state-space model of cross-region dynamic connectivity in MEG/EEG
Authors: Ying Yang, Elissa Aminoff, Michael Tarr, Robert E. Kass
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 |