Comparative_MEA_dataset/Codes/MATLAB spike detection/DetectSpikes_Amp.m

function [index,  spikes, thr, thrmax, noise_std_detect] = DetectSpikes_Amp(Data, handles)
% DetectSpikes_Amp Detect spikes based on amplitude thresholding. Adapted
% from WaveClus
% INPUT
%   Data - raw waveform from the electrode
%   handles - detection parameters
% OUTPUT
%   index - spike times (the only output written into result .csv files)
%   spikes - spike waveforms (not in use outside of the function currently)
%   thr - minimum threshold used for detection (not in use outside of the function)
%   thrmax - maximum threshold used for artefact removal (not in use outside of the function)
%   noise_std_detect - estimate of the standard deviation of the background
%   noise for detection (not in use outside of the function)


index_all=[];
spikes_all=[];

%% ACTUAL SAMPLE RATE BASED PRE/POST correction and REFRACTORY PERIOD calculation
% default: 24000Hz, pre 20 events, post 44 events, actual sr is read and
% the values are corrected
sr_factor = 24000;
handles.w_pre = ceil(handles.w_pre/sr_factor*handles.sr);      %number of pre-event data points stored (def. 20)
handles.w_post = ceil(handles.w_post/sr_factor*handles.sr);    %number of post-event data points stored (def. 44)
handles.ref = floor(handles.min_ref_per*handles.sr/1000);      %number of counts corresponding to the dead time 

%% SPIKE DETECTION 
[spikes,thr,thrmax, noise_std_detect, index]  = amp_detect(Data,handles);       %detection with amp. thresh. --> voiko Data olla taulukkona?

index = index';

index_all = [index_all index];
spikes_all = [spikes_all; spikes];
index = index_all/handles.sr;                 
spikes = spikes_all;



end