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+ | ===== FT_MVARANALYSIS ===== | ||
+ | |||
+ | Note that this reference documentation is identical to the help that is displayed in MATLAB when you type "help ft_mvaranalysis". | ||
+ | |||
+ | <html><pre> | ||
+ | <a href=/reference/ft_mvaranalysis><font color=green>FT_MVARANALYSIS</font></a> performs multivariate autoregressive modeling on | ||
+ | time series data over multiple trials. | ||
+ | |||
+ | Use as | ||
+ | [mvardata] = ft_mvaranalysis(cfg, data) | ||
+ | |||
+ | The input data should be organised in a structure as obtained from | ||
+ | the <a href=/reference/ft_preprocessing><font color=green>FT_PREPROCESSING</font></a> function. The configuration depends on the type | ||
+ | of computation that you want to perform. | ||
+ | The output is a data structure of datatype 'mvar' which contains the | ||
+ | multivariate autoregressive coefficients in the field coeffs, and the | ||
+ | covariance of the residuals in the field noisecov. | ||
+ | |||
+ | The configuration should contain: | ||
+ | cfg.method = the name of the toolbox containing the function for the | ||
+ | actual computation of the ar-coefficients | ||
+ | this can be 'biosig' (default) or 'bsmart' | ||
+ | you should have a copy of the specified toolbox in order | ||
+ | to use mvaranalysis (both can be downloaded directly). | ||
+ | cfg.mvarmethod = scalar (only required when cfg.method = 'biosig'). | ||
+ | default is 2, relates to the algorithm used for the | ||
+ | computation of the AR-coefficients by mvar.m | ||
+ | cfg.order = scalar, order of the autoregressive model (default=10) | ||
+ | cfg.channel = 'all' (default) or list of channels for which an mvar model | ||
+ | is fitted. (Do NOT specify if cfg.channelcmb is | ||
+ | defined) | ||
+ | cfg.channelcmb = specify channel combinations as a | ||
+ | two-column cell array with channels in each column between | ||
+ | which a bivariate model will be fit (overrides | ||
+ | cfg.channel) | ||
+ | cfg.keeptrials = 'no' (default) or 'yes' specifies whether the coefficients | ||
+ | are estimated for each trial seperately, or on the | ||
+ | concatenated data | ||
+ | cfg.jackknife = 'no' (default) or 'yes' specifies whether the coefficients | ||
+ | are estimated for all leave-one-out sets of trials | ||
+ | cfg.zscore = 'no' (default) or 'yes' specifies whether the channel data | ||
+ | are z-transformed prior to the model fit. This may be | ||
+ | necessary if the magnitude of the signals is very different | ||
+ | e.g. when fitting a model to combined MEG/EMG data | ||
+ | cfg.demean = 'yes' (default) or 'no' explicit removal of DC-offset | ||
+ | cfg.ems = 'no' (default) or 'yes' explicit removal ensemble mean | ||
+ | |||
+ | ft_mvaranalysis can be used to obtain one set of coefficients across | ||
+ | all time points in the data, also when the trials are of varying length. | ||
+ | |||
+ | ft_mvaranalysis can be also used to obtain time-dependent sets of | ||
+ | coefficients based on a sliding window. In this case the input cfg | ||
+ | should contain: | ||
+ | |||
+ | cfg.t_ftimwin = the width of the sliding window on which the coefficients | ||
+ | are estimated | ||
+ | cfg.toi = [t1 t2 ... tx] the time points at which the windows are | ||
+ | centered | ||
+ | |||
+ | To facilitate data-handling and distributed computing you can use | ||
+ | cfg.inputfile = ... | ||
+ | cfg.outputfile = ... | ||
+ | If you specify one of these (or both) the input data will be read from a *.mat | ||
+ | file on disk and/or the output data will be written to a *.mat file. These mat | ||
+ | files should contain only a single variable, corresponding with the | ||
+ | input/output structure. | ||
+ | |||
+ | See also <a href=/reference/ft_preprocessing><font color=green>FT_PREPROCESSING</font></a>, <a href=/reference/ft_sourcestatistics><font color=green>FT_SOURCESTATISTICS</font></a>, <a href=/reference/ft_freqstatistics><font color=green>FT_FREQSTATISTICS</font></a>, | ||
+ | <a href=/reference/ft_timelockstatistics><font color=green>FT_TIMELOCKSTATISTICS</font></a> | ||
+ | </pre></html> | ||