THE AUDITORY MODELING TOOLBOX

Applies to version: 0.10.0

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roenne2012_click - Simulate ABR respone to click

Program code:

function [waveVamp,waveVlat] = roenne2012_click(stim_level,varargin) 
%roenne2012_click  Simulate ABR respone to click
%   Usage: [waveVlat]  = roenne2012_click(stim_level).
%
%   Output parameters:
%     waveVlat   : Latency of simulated ABR wave V peak.
%     waveVamp   : Amplitude of simulated ABR wave V.
%
%   Input parameters:
%     stim_level      : Simulated levels. Default: Elberling et al. (2010)
%                       stimulus levels (20, 40, 60 dB HL) calibrated to pe
%                       SPL (+ 35.2 dB), see Rønne et al. (2012).   
%
%   ronne2012_click(stim_level) returns click evoked ABR wave V latencies
%   and amplitudes for a range of given stimulus levels. It simulates ABR
%   responses to click stimulus using the ABR model of Rønne et
%   al. (2012). The click stimulus is defined similar to Elberling et
%   al. (2010).
%
%   ---------
%
%   Please cite Rønne et al. (2012) and Zilany and Bruce (2007) if you use
%   this model.
%
%   References:
%     C. Elberling, J. Calloe, and M. Don. Evaluating auditory brainstem
%     responses to different chirp stimuli at three levels of stimulation. J.
%     Acoust. Soc. Am., 128(1):215--223, 2010.
%     
%     F. M. Rønne, T. Dau, J. Harte, and C. Elberling. Modeling auditory
%     evoked brainstem responses to transient stimuli. The Journal of the
%     Acoustical Society of America, 131(5):3903--3913, 2012. [1]http ]
%     
%     M. S. A. Zilany and I. C. Bruce. Representation of the vowel (epsilon)
%     in normal and impaired auditory nerve fibers: Model predictions of
%     responses in cats. J. Acoust. Soc. Am., 122(1):402--417, jul 2007.
%     
%     References
%     
%     1. http://scitation.aip.org/content/asa/journal/jasa/131/5/10.1121/1.3699171
%     
%
%   Url: http://amtoolbox.sourceforge.net/amt-0.10.0/doc/modelstages/roenne2012_click.php

% Copyright (C) 2009-2020 Piotr Majdak and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 0.10.0
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program.  If not, see <http://www.gnu.org/licenses/>.
  
%definput.keyvals.stim_level     = 40:10:100;
%[flags,kv] = ltfatarghelper({},definput,varargin);

fsmod       = 200e3;        % AN model fs.
modellength = 40;           % length of modelling [ms].

% load Unitary Response
[ur,fs]=data_roenne2012;

% Output filter corresponding to recording settings.
b=fir1(200,[100/(fs/2),3000/(fs/2)]);
a=1;

%% create click stimulus
% Load click stimulus (c0) from Elberling et al. (2010).
[stim,fsstim] = data_elberling2010('stim'); 

% Define length of stimulus, uses variable modellength.
refstim = zeros(modellength/1000*fs,1);                 

% Create stimulus with chirp stimulus and concatenated zeros => combined
% length = "modellength".
refstim(1:length(stim.c0)) = stim.c0;                                       


%% Simulate ABR - loop over stimulus levels
for L = 1:length(stim_level)
  lvl = stim_level(L);
  
  % call AN model
  ANdata = zilany2007(lvl, refstim, fsstim,fsmod);         
  
  % subtract 50 due to spontaneous rate. 
  ANout = ANdata'-50;                                       
  
  % Sum in time across fibers = summed activity pattern.
  ANsum = sum(ANout,2);                                     
  
  % Downsample ANsum to get fs = fs_UR = 32kHz.
  ANsum = resample(ANsum,fs,fsmod);                         
  
  % Simulated potential = UR * ANsum (* = convolved).
  simpot = filter(ur,1,ANsum);                               
  
  % apply output filter similar to the recording conditions in Elberling
  % et al. (2010).
  simpot = filtfilt(b,a,simpot);                             
  
  % Find max peak value (wave V).
  maxpeak = max(simpot);                                      
  
  % Find corresponding time of max peak value (latency of wave V).
  waveVlat(L)= find(simpot == max(simpot));              
  
  % find minimum in the interval from "max peak" to 6.7 ms later.
  minpeak = min(simpot(find(simpot == maxpeak):find(simpot == maxpeak)+100)); 
  
  % Calculate wave V amplitude, as the difference between the peak and
  % the following dip.
  waveVamp(L) = (maxpeak-minpeak);                           
  
end

% Subtract 15 ms as click stimulus peaks 15 ms into the time series
waveVlat = waveVlat/fs*1000-15;