Skip to content

neuralynx

Directory actions

More options

Directory actions

More options

Latest commit

 

History

History

neuralynx

README.md

Author Kristijan Armeni

Author: Kristijan Armeni

This folder contains unconnected recording (i.e. not attached to a patient) with the Neuralynx acquisition system (ATLAS, Pegasus 2.1.3.). The recording contributed by Ivan Skelin, Krembil Research Institute, University of Toronto.

Folder contents:

  • 7 .ncs files (continuously sampled signals, 2Khz sampling rate, except LAHCu1.ncs which is at 32Khz)
  • 7 .mat files (.mat version of .ncs files)
  • 1 .nev file (events file)

The 3 .ncs files (LAHC1.ncs, LAHC2.ncs, LAHC3.ncs) should represent an seeg signal. xAIR1.ncs and xEKG1.ncs represent a potential non-seeg modality (airflow and EKG in this case).
Two .ncs and .mat files (*_3_gaps*) were created with the matlab reader below to mimick .ncs acquisition with missing samples that read_raw_neuralynx() should handle.

Folder size: ~1MB For more info about the file formats, see: Neuralynx data file formats

MATLAB reader used

The .mat files were created via the Nlx2MatCSC.m (.ncs files) and Nlx2EV.mat (.nev file) readers, both version 5.0.0. See project page: https://www.urut.ch/new/serendipity/index.php?/pages/nlxtomatlab.html. Available at: https://neuralynx.fh-co.com/research-software/#file-converters-&-utilities

Example usage:

% for csc files
[Timestamps, ChannelNumbers, SampleFrequencies, NumberOfValidSamples, Samples, Header] = Nlx2MatCSC('LAHC1.ncs', [1 1 1 1 1] , 1, 1, 1);

% for events files
[Timestamps, EventIDs, TTLs, Extras, EventStrings, Header] = Nlx2MatEV('Events.nev', [1 1 1 1 1], 1, 1, []);

Creating .ncs channels with discontinuous samples (*_3_gaps.ncs)

The following custom MATLAB script was used to read in two .ncs channels (LAHC1, LAHC2), manually mark some samples as invalid, set them to 0, and save back to .ncs and .mat. These files are used in test_neuralynx_gaps().

datadir = "./";
chans = ["LAHC1", "LAHC2"];

for k = 1:length(chans)

    fname = fullfile(datadir, sprintf("%s.ncs", chans(k)));
    
    [Timestamps, ChannelNumbers, SampleFrequencies, NumberOfValidSamples, Samples, Header] = Nlx2MatCSC(char(fname), [1 1 1 1 1], 1, 1, 1);
    
    % create 3 gaps, each of different length
    modified_records = [10, 16, 21];
    modified_records_sizes = [100, 7, 23]; % 130 samples missing total
    
    for i = 1:length(modified_records)
        
        modified_record_id = modified_records(i);
        n_samps_invalid = modified_records_sizes(i); 

        % set "missing" samples as 0
        Samples(end-(n_samps_invalid-1):end, modified_record_id) = 0;
        
        % now adjust the indicator variable accordingly
        record_size = 512;
        NumberOfValidSamples(modified_record_id) = record_size - n_samps_invalid;
        
        assert(sum(Samples(:, modified_record_id) == 0) == n_samps_invalid);
    end
    
    % write matlab variables back to .ncs
    fn = sprintf("%s_3_gaps.ncs", chans(k));
    Mat2NlxCSC(char(fn), 0, 1, 1, [1 1 1 1 1 1], ...
                           Timestamps, ...
                           ChannelNumbers, ...
                           SampleFrequencies, ...
                           NumberOfValidSamples, ...
                           Samples, Header)
    
    % read the create .ncs back to matlab (for sanity check)
    [Timestamps, ChannelNumbers, SampleFrequencies, NumberOfValidSamples, Samples, Header] = Nlx2MatCSC(char(fn), [1 1 1 1 1], 1, 1, 1);
    
    % Now save back to matlab
    fnmat = fullfile(replace(fn, ".ncs", ".mat"));
    save(char(fnmat),...
         "Timestamps", ...
         "ChannelNumbers", ...
         "SampleFrequencies", ...
         "NumberOfValidSamples", ...
         "Samples", "Header");

end