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This repository contains the (Matlab / Python) toolbox created by the experience lab for physiological& position based data collections.

MATLAB 100.00%

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experiencelabpipeline's Issues

(Deconvolve EDA) Error when Nan at end of data

Every NaN value

Warning: All NaN entries in the first and second input were ignored. 
> In chckxy (line 104)
In pchip (line 55)
In sdeco_interimpulsefit (line 88)
In sdeconv_analysis (line 66)
In cgd_get_gradient (line 19)
In cgd (line 28)
In deconv_optimize (line 23)
In sdeco (line 105)
In leda_batchanalysis (line 111)
In Ledalab (line 32)
In deconvolve_eda (line 135)
In run_AweStudy (line 104) 

AT END OF DECONVOLUTION

Optimized parameter:  0.50	 4.00	 Error:    NaN (Initial parameter:  0.50	 4.00	 Error:    NaN)
Final optimized parameter:   1.00	  3.75	 Error:    NaN
Index exceeds the number of array elements. Index must not exceed 0.

Error in sdeco>deconv_apply (line 254)
    leda2.analysis.peakTime(iPeak) = t(minL(iPeak)) + mx_idx(1)/leda2.data.samplingrate;  %SCR peak could be outside of SC time range

Error in sdeco (line 106)
    deconv_apply;

Error in leda_batchanalysis (line 111)
                sdeco(args.optimize);

Error in Ledalab (line 32)
    leda_batchanalysis(varargin{:});

Error in deconvolve_eda (line 135)
Ledalab(strcat(cfg.tempdir, '\'), 'open', 'mat', 'analyze', 'CDA', 'optimize', 6, 'export_scrlist', [0.05 1]);

Error in run_AweStudy (line 104)
    data.empatica = deconvolve_eda(cfg, data.empatica);

(Deconvolve EDA) Error when Nan at start of data

The following error appears if there is a NaN value at the start of the array / input data (conductance)

Array indices must be positive integers or logical values.

Error in deconvolve_eda (line 91)

            data.conductance(starttime-1:endtime+1)   = data.conductance(endtime+1);

(ArtifactDetection) Suggest re-segmentation when file contains or ends with NaN values

The artiface tool, and artifact_eda currently allow free adding of NaN values. The expected behavior is:

  1. ArtifactCorrectionApp should check the output when you press Accept, and if there are NaN values at the end, then it should ask the user if they want to cut the data before sending it back.
  2. Artifact_eda should check if the new data is shorter than the rest of the data, and resegment the entire dataset to this new size

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