Function to read data from any file format supported by neuroshare

• should be general function that can work with any of the formats supported by neuroshare
• test and implement for cyberkinetics data

read_neuroshare function:

• read header
• read event
• read data (analog)
• read spike (timestamps and waveforms)

documentation:

• getting started with cyberkinetics data

%% read_header

tmp = read_neuroshare(filename);

% This returns a header structure with the following elements
%   hdr.Fs                  sampling frequency
%   hdr.nChans              number of channels
%   hdr.nSamples            number of samples per trial
%   hdr.nSamplesPre         number of pre-trigger samples in each trial
%   hdr.nTrials             number of trials
%   hdr.label               cell-array with labels of each channel
%   hdr.FirstTimeStamp      integer, only available for some subformats (mainly animal electrophysiology systems)
%   hdr.TimeStampPerSample  integer, only available for some subformats (mainly animal electrophysiology systems)
%
% Depending on the file format, additional header information can be
% returned in the hdr.orig subfield.

hdr = [];
hdr.Fs                  = tmp.hdr.seginfo(1).SampleRate; % take the fs from the first chan (assuming this is the same for all chans)
hdr.nChans              = tmp.hdr.fileinfo.EntityCount;
hdr.nSamples            = max([tmp.hdr.entityinfo.ItemCount]); % take the number of samples from the longest channel
hdr.nSamplesPre         = 0; % continuous data
hdr.nTrials             = 1; % continuous data
hdr.label               = {tmp.hdr.entityinfo.EntityLabel}; %%% contains non-unique chans

% FIXME: onderstaande gaat alleen als readspike == 'yes' %
hdr.FirstTimeStamp      = tmp.spikew.timestamp(1); % tmp.spiket.data(1) = same ???
hdr.TimeStampPerSample  = [];

%% read_event

tmp = read_neuroshare(filename, 'readevent', 'yes');

% This function returns an event structure with the following fields
%   event.type      = string
%   event.sample    = expressed in samples, the first sample of a recording is 1
%   event.value     = number or string
%   event.offset    = expressed in samples
%   event.duration  = expressed in samples
%   event.timestamp = expressed in timestamp units, which vary over systems (optional)
%
% The event type and sample fields are always defined, other fields can be empty,
% depending on the type of event file. Events are sorted by the sample on
% which they occur.

for i=1:length(tmp.hdr.eventinfo)
    event(i).type      = tmp.hdr.eventinfo(i).EventType;
    event(i).value     = tmp.event.data(i);
    event(i).timestamp = tmp.event.timestamp(i);
    event(i).sample    = tmp.event.sample(i);
end

% FIXME: i dont understand the neuroshare event structure yet...
% for i=1:length(tmp.event.timestamp)
%     event(i).type      = tmp.hdr.eventinfo(i).EventType;
%     event(i).value     = tmp.event.data(i);
%     event(i).timestamp = tmp.event.timestamp(i);
%     event(i).sample    = tmp.event.sample(i);
% end

%% read_data

tmp = read_neuroshare(filename, 'readanalog', 'yes', 'chanindx', chanindx, 'begsample', begsample, 'endsample', endsample);

% This function returns a 2-D matrix of size Nchans*Nsamples for
% continuous data when begevent and endevent are specified, or a 3-D
% matrix of size Nchans*Nsamples*Ntrials for epoched or trial-based
% data when begtrial and endtrial are specified.

dat = tmp.analog.data';

%% read_spike

tmp = read_neuroshare(filename, 'readspike', 'yes');

% The output spike structure contains
%   spike.label     = 1xNchans cell-array, with channel labels
%   spike.waveform  = 1xNchans cell-array, each element contains a matrix (Nsamples X Nspikes)
%   spike.timestamp = 1xNchans cell-array, each element contains a vector (1 X Nspikes)
%   spike.unit      = 1xNchans cell-array, each element contains a vector (1 X Nspikes)


spike.label = {tmp.hdr.entityinfo(tmp.list.segment).EntityLabel};
for i=1:length(spike.label)
    spike.waveform{i}  = tmp.spikew.data(:,:,i);
    spike.timestamp{i} = tmp.spikew.timestamp(:,i)';
    spike.unit{i}      = tmp.spikew.unitID(:,i)';
end

% FIXME: spiket is somehow similar to spikew.timestamp, but without the nan/zero elements...?

http://neuroshare.sourceforge.net/DLLLinks.shtml