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
Citation
"Dynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise", PLOS Computational Biology (2020), in press
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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)
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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)
- 2-speakers (Sp1+Sp2, Sp1 attended from 0-28 s, Sp2 attended from 30-60s)
- 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