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chronux

PURPOSE ^

This library performs time-frequency analysis (mostly using the

SYNOPSIS ^

function chronux

DESCRIPTION ^

 This library performs time-frequency analysis (mostly using the
 multi-taper spectral estimation method) of univariate and multivariate
 data, both for continuous processes such as LFP/EEG and for point
 processes such as spike times. Point process can either be stored as
 times or as a binned process of counts. The routines in this library
 are named differently for the three cases. For calculations (e.g. spectrum)
 that can be performed for each of the three data types, we use suffixes
 c, pb, or pt to refer to continuous, point process binned counts, or
 point process times. For example, the spectrum calculation is performed
 mtspectrumc for continuous processes, mtspectrumpb for a binned point
 process, and mtspectrumpt for a point process consisting of times. There
 are also routines for calculating hybrid quantities involving one continuous
 and one point process. These are suffixed in a similar manner. For
 example, coherencypbc calculates the coherency between a binned point process
 and a continuous process.
 
 Certain variables are used repeatedly in this library.

 DATA
 data in most cases can be univariate or multivariate, and either point process, 
 or continuous.

      Continuous data: Continuous data is assumed to be a matrix with 
                       dimensions samples x channels/trials.

      Point Process: A single time series of spike times can be in the form of 
                     a column vector.
                     Multichannel/trial spike time data is not amenable to this 
                     storage format, since there are generally different 
                     number of spikes in each channel/trial. Instead, 
                     multichannel/trial spike data is stored in a structure 
                     array. A structure is a matlab data object with various 
                     fields. These fields contain the elements
                       e.g. The command data=struct('times',[]); creates an empty 
                            structure with field 'times'. Similarly, the command
                            data=struct('times',[1 2 3]); creates the structure with
                            the field 'times' containing integers 1, 2, and 3. 
        
                     We can also have a structure array (or an array of structures)
                     defined for example, by
                     data(1)=struct('times',rand(1,100)); and
                     data(2)=struct('times',rand(1,200));
                     This is a 2 dimensional structure array where the
                     first field is a 100 dimensional random vector, and
                     the second field is a 200 dimensional random vector.
                     This format allows storage of multichannel point
                     process times in a single variable data.
                     
                     The above holds for point processes stored as times.
                     If instead, the point processes are binned, then one
                     can use a matrix to represent them 
                     

      Summary: data - array of continuous data with dimensions time x channels
                      structural array of spike times with dimensions
                               equal to the number of channels
                      1d array of spike times as a column vector
                      array of binned spike counts with dimensions time x channels

 TIME-FREQUENCY PARAMETERS:
 These are various parameters used in the spectral calculations.

 Fs=used to denote sampling frequency

 E=used to denote events for calculating event triggered spectra. 

 win=used to denote windows around events with which to calculate event
     triggered quantities. specified in the form [winl winr] to
     calculate the relevant quantity using chunks of data starting winl
     before event and ending winr after event. 

 movingwin: moving window parameters used to calculate spectrograms, etc.
            in the form [window winstep]. window denotes the duration of
            the window used to compute the fft, and winstep denotes the
            step size. 

 pad: pad factor used to compute fft. This should be chosen to be a small
      integer 2,3. Then the fft will be calculated using 2 raised to (the
      next power of 2 greater than the length of the data+pad). e.g. if
      the length of the data is 512 and pad=2, fft will be calculated
      using windows of length 2048. If pad=3, it will be calculated using
      4096 points.

 fpass: frequencies in an fft calculation can range from 0 to Fs/2 where
        Fs is the sampling frequency. Sometimes it may be useful to
        compute fourier transforms (and resulting quantities like the
        spectrum over a smaller range of frequencies). This is specified
        by fpass, which can be in the form [fmin fmax] where fmin >=0 and
        fmax<=Fs/2

 t: time

 f: frequency.

 Note that the units of all temporal parameters have to be consistent. So, 
 if Fs is in Hz, then E,win,movingwin,t have to be in secs, and f is in Hz. 
 Alternately, E,win,movingwin,t are in number of samples, then Fs has to be
 specified at 1, and f is in inverse sample units. Also, units for spike
 times have to be consistent with the other temporal quantities. Strange
 behavior and crashes may result if (for example) spike times are in
 seconds and the sampling frequency Fs is specified to be 1.

 DPSS PARAMETERS:
 dpss is a matlab function we use to calculate the slepian sequences.
 
 tapers: tapers denotes either the parameters used in the dpss calculation
         or the calculated slepian sequences themselves. In the first case,
         tapers=[TW K] where TW denotes the time bandwidth product, and K denotes 
         the number of sequences to be calculated.

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function chronux
0002 % This library performs time-frequency analysis (mostly using the
0003 % multi-taper spectral estimation method) of univariate and multivariate
0004 % data, both for continuous processes such as LFP/EEG and for point
0005 % processes such as spike times. Point process can either be stored as
0006 % times or as a binned process of counts. The routines in this library
0007 % are named differently for the three cases. For calculations (e.g. spectrum)
0008 % that can be performed for each of the three data types, we use suffixes
0009 % c, pb, or pt to refer to continuous, point process binned counts, or
0010 % point process times. For example, the spectrum calculation is performed
0011 % mtspectrumc for continuous processes, mtspectrumpb for a binned point
0012 % process, and mtspectrumpt for a point process consisting of times. There
0013 % are also routines for calculating hybrid quantities involving one continuous
0014 % and one point process. These are suffixed in a similar manner. For
0015 % example, coherencypbc calculates the coherency between a binned point process
0016 % and a continuous process.
0017 %
0018 % Certain variables are used repeatedly in this library.
0019 %
0020 % DATA
0021 % data in most cases can be univariate or multivariate, and either point process,
0022 % or continuous.
0023 %
0024 %      Continuous data: Continuous data is assumed to be a matrix with
0025 %                       dimensions samples x channels/trials.
0026 %
0027 %      Point Process: A single time series of spike times can be in the form of
0028 %                     a column vector.
0029 %                     Multichannel/trial spike time data is not amenable to this
0030 %                     storage format, since there are generally different
0031 %                     number of spikes in each channel/trial. Instead,
0032 %                     multichannel/trial spike data is stored in a structure
0033 %                     array. A structure is a matlab data object with various
0034 %                     fields. These fields contain the elements
0035 %                       e.g. The command data=struct('times',[]); creates an empty
0036 %                            structure with field 'times'. Similarly, the command
0037 %                            data=struct('times',[1 2 3]); creates the structure with
0038 %                            the field 'times' containing integers 1, 2, and 3.
0039 %
0040 %                     We can also have a structure array (or an array of structures)
0041 %                     defined for example, by
0042 %                     data(1)=struct('times',rand(1,100)); and
0043 %                     data(2)=struct('times',rand(1,200));
0044 %                     This is a 2 dimensional structure array where the
0045 %                     first field is a 100 dimensional random vector, and
0046 %                     the second field is a 200 dimensional random vector.
0047 %                     This format allows storage of multichannel point
0048 %                     process times in a single variable data.
0049 %
0050 %                     The above holds for point processes stored as times.
0051 %                     If instead, the point processes are binned, then one
0052 %                     can use a matrix to represent them
0053 %
0054 %
0055 %      Summary: data - array of continuous data with dimensions time x channels
0056 %                      structural array of spike times with dimensions
0057 %                               equal to the number of channels
0058 %                      1d array of spike times as a column vector
0059 %                      array of binned spike counts with dimensions time x channels
0060 %
0061 % TIME-FREQUENCY PARAMETERS:
0062 % These are various parameters used in the spectral calculations.
0063 %
0064 % Fs=used to denote sampling frequency
0065 %
0066 % E=used to denote events for calculating event triggered spectra.
0067 %
0068 % win=used to denote windows around events with which to calculate event
0069 %     triggered quantities. specified in the form [winl winr] to
0070 %     calculate the relevant quantity using chunks of data starting winl
0071 %     before event and ending winr after event.
0072 %
0073 % movingwin: moving window parameters used to calculate spectrograms, etc.
0074 %            in the form [window winstep]. window denotes the duration of
0075 %            the window used to compute the fft, and winstep denotes the
0076 %            step size.
0077 %
0078 % pad: pad factor used to compute fft. This should be chosen to be a small
0079 %      integer 2,3. Then the fft will be calculated using 2 raised to (the
0080 %      next power of 2 greater than the length of the data+pad). e.g. if
0081 %      the length of the data is 512 and pad=2, fft will be calculated
0082 %      using windows of length 2048. If pad=3, it will be calculated using
0083 %      4096 points.
0084 %
0085 % fpass: frequencies in an fft calculation can range from 0 to Fs/2 where
0086 %        Fs is the sampling frequency. Sometimes it may be useful to
0087 %        compute fourier transforms (and resulting quantities like the
0088 %        spectrum over a smaller range of frequencies). This is specified
0089 %        by fpass, which can be in the form [fmin fmax] where fmin >=0 and
0090 %        fmax<=Fs/2
0091 %
0092 % t: time
0093 %
0094 % f: frequency.
0095 %
0096 % Note that the units of all temporal parameters have to be consistent. So,
0097 % if Fs is in Hz, then E,win,movingwin,t have to be in secs, and f is in Hz.
0098 % Alternately, E,win,movingwin,t are in number of samples, then Fs has to be
0099 % specified at 1, and f is in inverse sample units. Also, units for spike
0100 % times have to be consistent with the other temporal quantities. Strange
0101 % behavior and crashes may result if (for example) spike times are in
0102 % seconds and the sampling frequency Fs is specified to be 1.
0103 %
0104 % DPSS PARAMETERS:
0105 % dpss is a matlab function we use to calculate the slepian sequences.
0106 %
0107 % tapers: tapers denotes either the parameters used in the dpss calculation
0108 %         or the calculated slepian sequences themselves. In the first case,
0109 %         tapers=[TW K] where TW denotes the time bandwidth product, and K denotes
0110 %         the number of sequences to be calculated.
0111 
0112 %
0113 % AVERAGING AND ERRORS
0114 % Chronux calculates theoretical and Jacknife error bars on most spectral
0115 % quantities. Averaging behavior and error calculation are controlled by
0116 % two parameters, trialave and err
0117 %
0118 % err=[errtype p] calculates theoretical error bars (confidence levels)
0119 % when errtype=1 and jackknife error bars when errchk=2. In each case, the
0120 % error is calculated at a p value specified by p.
0121 %
0122 % trialave: trialave controls whether or not to average over channels/trials for
0123 % multichannel/trial analyses. trialave=0 (default) implies no trial
0124 % averaging, trialave=1 implies that the quantity of interest is averaged
0125 % over trials.

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