You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_fmri_spm_ui.m
    path of file: ./repos/ExploreASL/External/SPMmodified
    
    the code of the file until where you have to start completion: function [SPM] = spm_fmri_spm_ui(SPM)
% Setting up the general linear model for fMRI time-series
% FORMAT [SPM] = spm_fmri_spm_ui(SPM)
%
% creates SPM with the following fields
%
%       xY: [1x1 struct] - data structure
%    nscan: [double]     - vector of scans per session
%      xBF: [1x1 struct] - Basis function structure   (see spm_fMRI_design)
%     Sess: [1x1 struct] - Session structure          (see spm_fMRI_design)
%       xX: [1x1 struct] - Design matrix structure    (see spm_fMRI_design)
%      xGX: [1x1 struct] - Global variate structure
%      xVi: [1x1 struct] - Non-sphericity structure
%       xM: [1x1 struct] - Masking structure
%    xsDes: [1x1 struct] - Design description structure
%
%
%     SPM.xY
%             P: [n x ? char]       - filenames
%            VY: [n x 1 struct]     - filehandles
%            RT: Repeat time
%
%    SPM.xGX
%
%       iGXcalc: {'none'|'Scaling'} - Global normalization option
%       sGXcalc: 'mean voxel value' - Calculation method
%        sGMsca: 'session specific' - Grand mean scaling
%            rg: [n x 1 double]     - Global estimate
%            GM: 100                - Grand mean
%           gSF: [n x 1 double]     - Global scaling factor
%
%    SPM.xVi
%            Vi: {[n x n sparse]..} - covariance components
%          form: {'none'|'AR(1)'}   - form of non-sphericity
%
%     SPM.xM
%             T: [n x 1 double]     - Masking index
%            TH: [n x 1 double]     - Threshold
%             I: 0
%            VM:                    - Mask filehandles
%            xs: [1x1 struct]       - cellstr description
%
%__________________________________________________________________________
%
% spm_fmri_spm_ui configures the design matrix, data specification and
% filtering that specify the ensuing statistical analysis. These arguments
% are passed to spm_spm that then performs the actual parameter estimation.
%
% The design matrix defines the experimental design and the nature of
% hypothesis testing to be implemented.  The design matrix has one row for
% each scan and one column for each effect or explanatory variable (e.g.
% regressor or stimulus function).  The parameters are estimated in a least
% squares sense using the general linear model.  Specific profiles within
% these parameters are tested using a linear compound or contrast with the
% T or F statistic.  The resulting statistical map constitutes an SPM.  The
% SPM{T}/{F} is then characterized in terms of focal or regional
% differences by assuming that (under the null hypothesis) the components
% of the SPM (i.e. residual fields) behave as smooth stationary Gaussian
% fields.
%
% spm_fmri_spm_ui allows you to (i) specify a statistical model in terms of
% a design matrix, (ii) associate some data with a pre-specified design [or
% (iii) specify both the data and design] and then proceed to estimate the
% parameters of the model.
% Inferences can be made about the ensuing parameter estimates (at a first
% or fixed-effect level) in the results section, or they can be re-entered
% into a second (random-effect) level analysis by treating the session or
% subject-specific [contrasts of] parameter estimates as new summary data.
% Inferences at any level are obtained by specifying appropriate T or F
% contrasts in the results section to produce SPMs and tables of p values
% and statistics.
%
% spm_fmri_spm calls spm_fMRI_design which allows you to configure a design
% matrix in terms of events or epochs.
%
% spm_fMRI_design allows you to build design matrices with separable
% session-specific partitions.  Each partition may be the same (in which
% case it is only necessary to specify it once) or different.  Responses
% can be either event- or epoch related, The only distinction is the
% duration of the underlying input or stimulus function. Mathematically
% they are both modelled by convolving a series of delta (stick) or box
% functions (u), indicating the onset of an event or epoch with a set of
% basis functions.  These basis functions model the hemodynamic
% convolution, applied by the brain, to the inputs.  This convolution can
% be first-order or a generalized convolution modelled to second order (if
% you specify the Volterra option). [The same inputs are used by the
% hemodynamic model or or dynamic causal models which model the convolution
% explicitly in terms of hidden state variables (see spm_hdm_ui and
% spm_dcm_ui).]
% Basis functions can be used to plot estimated responses to single events
% once the parameters (i.e. basis function coefficients) have been
% estimated.  The importance of basis functions is that they provide a
% graceful transition between simple fixed response models (like the
% box-car) and finite impulse response (FIR) models, where there is one
% basis function for each scan following an event or epoch onset.  The nice
% thing about basis functions, compared to FIR models, is that data
% sampling and stimulus presentation does not have to be synchronized
% thereby allowing a uniform and unbiased sampling of peri-stimulus time.

    
function [SPM] = spm_fmri_spm_ui(SPM)
% Setting up the general linear model for fMRI time-series
% FORMAT [SPM] = spm_fmri_spm_ui(SPM)
%
% creates SPM with the following fields
%
%       xY: [1x1 struct] - data structure
%    nscan: [double]     - vector of scans per session
%      xBF: [1x1 struct] - Basis function structure   (see spm_fMRI_design)
%     Sess: [1x1 struct] - Session structure          (see spm_fMRI_design)
%       xX: [1x1 struct] - Design matrix structure    (see spm_fMRI_design)
%      xGX: [1x1 struct] - Global variate structure
%      xVi: [1x1 struct] - Non-sphericity structure
%       xM: [1x1 struct] - Masking structure
%    xsDes: [1x1 struct] - Design description structure
%
%
%     SPM.xY
%             P: [n x ? char]       - filenames
%            VY: [n x 1 struct]     - filehandles
%            RT: Repeat time
%
%    SPM.xGX
%
%       iGXcalc: {'none'|'Scaling'} - Global normalization option
%       sGXcalc: 'mean voxel value' - Calculation method
%        sGMsca: 'session specific' - Grand mean scaling
%            rg: [n x 1 double]     - Global estimate
%            GM: 100                - Grand mean
%           gSF: [n x 1 double]     - Global scaling factor
%
%    SPM.xVi
%            Vi: {[n x n sparse]..} - covariance components
%          form: {'none'|'AR(1)'}   - form of non-sphericity
%
%     SPM.xM
%             T: [n x 1 double]     - Masking index
%            TH: [n x 1 double]     - Threshold
%             I: 0
%            VM:                    - Mask filehandles
%            xs: [1x1 struct]       - cellstr description
%
%__________________________________________________________________________
%
% spm_fmri_spm_ui configures the design matrix, data specification and
% filtering that specify the ensuing statistical analysis. These arguments
% are passed to spm_spm that then performs the actual parameter estimation.
%
% The design matrix defines the experimental design and the nature of
% hypothesis testing to be implemented.  The design matrix has one row for
% each scan and one column for each effect or explanatory variable (e.g.
% regressor or stimulus function).  The parameters are estimated in a least
% squares sense using the general linear model.  Specific profiles within
% these parameters are tested using a linear compound or contrast with the
% T or F statistic.  The resulting statistical map constitutes an SPM.  The
% SPM{T}/{F} is then characterized in terms of focal or regional
% differences by assuming that (under the null hypothesis) the components
% of the SPM (i.e. residual fields) behave as smooth stationary Gaussian
% fields.
%
% spm_fmri_spm_ui allows you to (i) specify a statistical model in terms of
% a design matrix, (ii) associate some data with a pre-specified design [or
% (iii) specify both the data and design] and then proceed to estimate the
% parameters of the model.
% Inferences can be made about the ensuing parameter estimates (at a first
% or fixed-effect level) in the results section, or they can be re-entered
% into a second (random-effect) level analysis by treating the session or
% subject-specific [contrasts of] parameter estimates as new summary data.
% Inferences at any level are obtained by specifying appropriate T or F
% contrasts in the results section to produce SPMs and tables of p values
% and statistics.
%
% spm_fmri_spm calls spm_fMRI_design which allows you to configure a design
% matrix in terms of events or epochs.
%
% spm_fMRI_design allows you to build design matrices with separable
% session-specific partitions.  Each partition may be the same (in which
% case it is only necessary to specify it once) or different.  Responses
% can be either event- or epoch related, The only distinction is the
% duration of the underlying input or stimulus function. Mathematically
% they are both modelled by convolving a series of delta (stick) or box
% functions (u), indicating the onset of an event or epoch with a set of
% basis functions.  These basis functions model the hemodynamic
% convolution, applied by the brain, to the inputs.  This convolution can
% be first-order or a generalized convolution modelled to second order (if
% you specify the Volterra option). [The same inputs are used by the
% hemodynamic model or or dynamic causal models which model the convolution
% explicitly in terms of hidden state variables (see spm_hdm_ui and
% spm_dcm_ui).]
% Basis functions can be used to plot estimated responses to single events
% once the parameters (i.e. basis function coefficients) have been
% estimated.  The importance of basis functions is that they provide a
% graceful transition between simple fixed response models (like the
% box-car) and finite impulse response (FIR) models, where there is one
% basis function for each scan following an event or epoch onset.  The nice
% thing about basis functions, compared to FIR models, is that data
% sampling and stimulus presentation does not have to be synchronized
% thereby allowing a uniform and unbiased sampling of peri-stimulus time.
%
% Event-related designs may be stochastic or deterministic.  Stochastic
% designs involve one of a number of trial-types occurring with a specified
% probably at successive intervals in time.  These probabilities can be
% fixed (stationary designs) or time-depend