Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise
Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise
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Date
2020-08-02
Authors
Presacco, Alessandro
Miran, Sina
Fu, Michael
Marcus, Steven
Advisor
Jonathan, Simon
Babadi, Betash
Babadi, Betash
Citation
"Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise", PLOS Computational Biology (2020), in press
DRUM DOI
Abstract
MEG data used for the "Switching attention" experiment
Notes
MEG data were collected from 157 sensors + 35 additional channels (158 - 192) used for reference and trigger. Each mat file contains information about the data, such as sampling frequency. A 3D matrix is used to store the 3 repetitions recorded.