Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise

No Thumbnail Available

Files

Instructed Attention Switch Experiment.zip (628.65 MB)
No. of downloads: 52

Publication or External Link

Date

2020-08-02

Authors

Advisor

Simon, Jonathan
Babadi, Behtash

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

https://doi.org/10.13016/rqvl-apnu

Abstract

MEG data used for the "Switching attention" experiment. This set of data refers to the part of the "forced" switching of attention

Notes

Experiment Description:

MEG signals recorded in a magnetically sheilded room from a whole-head recording system with 157 channels. Initial sampling resolution: 1000 Hz Downsampling rate: 5 (downsamples to 200 Hz)

  • Attention Switch: Two speakers peresented monaurally (7 subjects). Listeners attenended to one of the speakers for 0-28 s and switched to the other speaker for 30-60 s. (a pause presented at 28-30 s as a switching cue)

    1. 2-speakers (Sp1+Sp2, Sp1 attended from 0-28 s, Sp2 attended from 30-60s)
    2. 2-speakers (Sp1+Sp2, Sp2 attended from 0-28 s, Sp1 attended from 30-60s)

Folders:

  • Speech: original audio files
  • MEG:Neural data

Data Structure:

  • data_DSSDomain: Data in DSS domain time (1 min) * DSS channels * trials
  • data_DSSed: Clean data projected back into the sensor domain time (1 min) * sensor channels * trials

URI (handle)

http://hdl.handle.net/1903/26352

Rights

Collections

Institute for Systems Research Technical Reports