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_dcm_ssr_results.m
    path of file: ./repos/malini/Codes/deconvolution - model/spm8/toolbox/dcm_meeg
    
    the code of the file until where you have to start completion: function [DCM] = spm_dcm_ssr_results(DCM,Action)
% Results for ERP Dynamic Causal Modeling (DCM)
% FORMAT spm_dcm_erp_results(DCM,'spectral data');
% FORMAT spm_dcm_erp_results(DCM,'Coupling (A)');
% FORMAT spm_dcm_erp_results(DCM,'Coupling (B)');
% FORMAT spm_dcm_erp_results(DCM,'Coupling (C)');
% FORMAT spm_dcm_erp_results(DCM,'trial-specific effects');
% FORMAT spm_dcm_erp_results(DCM,'Input');
% FORMAT spm_dcm_erp_results(DCM,'Cross-spectral density');
% FORMAT spm_dcm_erp_results(DCM,'Dipoles');
%                
%___________________________________________________________________________
%

    
function [DCM] = spm_dcm_ssr_results(DCM,Action)
% Results for ERP Dynamic Causal Modeling (DCM)
% FORMAT spm_dcm_erp_results(DCM,'spectral data');
% FORMAT spm_dcm_erp_results(DCM,'Coupling (A)');
% FORMAT spm_dcm_erp_results(DCM,'Coupling (B)');
% FORMAT spm_dcm_erp_results(DCM,'Coupling (C)');
% FORMAT spm_dcm_erp_results(DCM,'trial-specific effects');
% FORMAT spm_dcm_erp_results(DCM,'Input');
% FORMAT spm_dcm_erp_results(DCM,'Cross-spectral density');
% FORMAT spm_dcm_erp_results(DCM,'Dipoles');
%                
%___________________________________________________________________________
%
% DCM is a causal modelling procedure for dynamical systems in which
% causality is inherent in the differential equations that specify the model.
% The basic idea is to treat the system of interest, in this case the brain,
% as an input-state-output system.  By perturbing the system with known
% inputs, measured responses are used to estimate various parameters that
% govern the evolution of brain states.  Although there are no restrictions
% on the parameterisation of the model, a bilinear approximation affords a
% simple re-parameterisation in terms of effective connectivity.  This
% effective connectivity can be latent or intrinsic or, through bilinear
% terms, model input-depend