TinnitusReconstructor

Reverse correlation using linear regression and compressed sensing for uncovering the psychoacoustic tinnitus spectrum

https://github.com/the-lammert-lab/tinnitusreconstructor.jl

Science Score: 44.0%

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Keywords

biomedical-engineering compressed-sensing psychophysics reverse-correlation tinnitus
Last synced: 6 months ago · JSON representation ·

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Reverse correlation using linear regression and compressed sensing for uncovering the psychoacoustic tinnitus spectrum

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  • Stars: 3
  • Watchers: 2
  • Forks: 0
  • Open Issues: 2
  • Releases: 4
Topics
biomedical-engineering compressed-sensing psychophysics reverse-correlation tinnitus
Created about 3 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

TinnitusReconstructor

Stable Dev Build Status Coverage ColPrac: Contributor's Guide on Collaborative Practices for Community Packages SciML Code Style

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

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}
}

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  • Total versions: 8
juliahub.com: TinnitusReconstructor

Reverse correlation using linear regression and compressed sensing for uncovering the psychoacoustic tinnitus spectrum

  • Versions: 8
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 9.8%
Dependent packages count: 38.4%
Average: 39.4%
Stargazers count: 54.7%
Forks count: 54.7%
Last synced: 6 months ago