TinnitusReconstructor
Reverse correlation using linear regression and compressed sensing for uncovering the psychoacoustic tinnitus spectrum
Science Score: 44.0%
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Keywords
Repository
Reverse correlation using linear regression and compressed sensing for uncovering the psychoacoustic tinnitus spectrum
Basic Info
- Host: GitHub
- Owner: The-Lammert-Lab
- License: agpl-3.0
- Language: Julia
- Default Branch: main
- Homepage: https://the-lammert-lab.github.io/TinnitusReconstructor.jl/
- Size: 1.38 MB
Statistics
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 2
- Releases: 4
Topics
Metadata Files
README.md
TinnitusReconstructor
This package provides both a range of auditory stimulus generation techniques as well as reverse correlation reconstruction (RC) functions.
The stimulus generation methods are intended to be used in RC experimental paradigms related to reconstructing the internal representations of tinnitus. However, the functionality is generic and may be useful in many other scenarios.
Example usage
Create a stimgen struct using some default and some custom parameters. ```julia using TinnitusReconstructor stimgen = GaussianPrior(; nbins=80, nbinsfilledmean=20, nbinsfilled_var=0.01, )
GaussianPrior{Float64, Int64}(100.0, 22000.0, 0.5, 44100.0, 0.01, 20, 80)
```
Load in the target sound and convert to binned representation ```julia using FileIO: load import LibSndFile audiofile = "ATA/ATATinnitusBuzzingTone1sec.wav" # File path. audio = wav2spect(load(audiofile)) # Read in file, truncate to 0.5s, convert to spectrum. target_signal = 10 * log10.(audio) # Convert to dB
Convert to binned representation that matches the number of stimgen bins
binnedtargetsignal = spect2binnedrepr(stimgen, target_signal) ```
Generate 500 random stimuli and simulate ideal responses for each.
Then, compute the linear (gs) and compressed sensing (cs) reconstructions
and correlate the reconstruction with the binned target signal to determine reconstruction quality.
julia
using Statistics
responses, _, stim = subject_selection_process(stimgen, target_signal, 500)
recon_linear = gs(responses, stim')
recon_cs = cs(responses, stim')
r_linear = cor(recon_linear, binned_target_signal)
r_cs = cor(recon_cs, binned_target_signal)
Citation
@article{Hoyland2023,
author={Hoyland, Alec and Barnett, Nelson V. and Roop, Benjamin W. and Alexandrou, Danae and Caplan, Myah and Mills, Jacob and Parrell, Benjamin and Chari, Divya A. and Lammert, Adam C.},
journal={IEEE Open Journal of Engineering in Medicine and Biology},
title={Reverse Correlation Uncovers More Complete Tinnitus Spectra},
year={2023},
volume={4},
number={},
pages={116-118},
doi={10.1109/OJEMB.2023.3275051}
}
Owner
- Name: The Lammert Lab
- Login: The-Lammert-Lab
- Kind: organization
- Repositories: 1
- Profile: https://github.com/The-Lammert-Lab
The Brain, Behavior, and Computation Lab at Worcester Polytechnic Institute
Citation (CITATION.bib)
@article{Hoyland2023,
author={Hoyland, Alec and Barnett, Nelson V. and Roop, Benjamin W. and Alexandrou, Danae and Caplan, Myah and Mills, Jacob and Parrell, Benjamin and Chari, Divya A. and Lammert, Adam C.},
journal={IEEE Open Journal of Engineering in Medicine and Biology},
title={Reverse Correlation Uncovers More Complete Tinnitus Spectra},
year={2023},
volume={4},
number={},
pages={116-118},
doi={10.1109/OJEMB.2023.3275051}
}
GitHub Events
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Packages
- Total packages: 1
- Total downloads: unknown
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 8
juliahub.com: TinnitusReconstructor
Reverse correlation using linear regression and compressed sensing for uncovering the psychoacoustic tinnitus spectrum
- Homepage: https://the-lammert-lab.github.io/TinnitusReconstructor.jl/
- Documentation: https://docs.juliahub.com/General/TinnitusReconstructor/stable/
- License: AGPL-3.0
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Latest release: 0.3.3
published over 1 year ago