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_eeg_invert_prepro.m
    path of file: ./repos/spm
    
    the code of the file until where you have to start completion: function [D] = spm_eeg_invert_prepro(D,val)
%
%% Preprocessing for inversion stage. 
%% includes spatial and temporal dimension reduction
%
%% this version only handles single subject single modality data
%% the removal of many scaling factors makes it easier to compare between forward models
% ReML inversion of multiple forward models for EEG-MEG
% FORMAT [D] = spm_eeg_invert_classic(D)
% ReML estimation of regularisation hyperparameters using the
% spatiotemporal hierarchy implicit in EEG/MEG data
%
% Requires:
% D{i}.inv{val}.inverse:
%
%     inverse.modality - modality to use in case of multimodal datasets
%
%     inverse.trials - D.events.types to invert
%     inverse.type   - 'GS' Greedy search on MSPs
%                      'ARD' ARD search on MSPs
%                      'MSP' GS and ARD multiple sparse priors
%                      'LOR' LORETA-like model
%                      'IID' minimum norm
%                      'EBB' for empirical bayes beamformer

%    inverse.priors{} - a cell array of anatomical and functional priors to be
%    considered
%
%     inverse.woi    - time window of interest ([start stop] in ms)
%     inverse.lpf    - band-pass filter - low frequency cut-off (Hz)
%     inverse.hpf    - band-pass filter - high frequency cut-off (Hz)
%     inverse.Han    - switch for Hanning window
%     inverse.xyz    - (n x 3) locations of spherical VOIs
%     inverse.rad    - radius (mm) of VOIs
%
%     inverse.Nm     - maximum number of channel modes
%     inverse.Nr     - maximum number of temporal modes
%     inverse.Np     - number of sparse priors per hemisphere
%     inverse.smooth - smoothness of source priors (0 to 1)
%     inverse.Na     - number of most energetic dipoles
%     inverse.sdv    - standard deviations of Gaussian temporal correlation
%     inverse.Qe     - any sensor error components (e.g. empty-room data)
%     inverse.Qe0     - minimum amount of sensor noise power relative to
%                        signal eg 0.1 would correspond to power SNR of 10.0
%     inverse.A       - predefined spatial modes (Nchans*Nmodes) to project
%                       sensor data through

    
function [D] = spm_eeg_invert_prepro(D,val)
%
%% Preprocessing for inversion stage. 
%% includes spatial and temporal dimension reduction
%
%% this version only handles single subject single modality data
%% the removal of many scaling factors makes it easier to compare between forward models
% ReML inversion of multiple forward models for EEG-MEG
% FORMAT [D] = spm_eeg_invert_classic(D)
% ReML estimation of regularisation hyperparameters using the
% spatiotemporal hierarchy implicit in EEG/MEG data
%
% Requires:
% D{i}.inv{val}.inverse:
%
%     inverse.modality - modality to use in case of multimodal datasets
%
%     inverse.trials - D.events.types to invert
%     inverse.type   - 'GS' Greedy search on MSPs
%                      'ARD' ARD search on MSPs
%                      'MSP' GS and ARD multiple sparse priors
%                      'LOR' LORETA-like model
%                      'IID' minimum norm
%                      'EBB' for empirical bayes beamformer

%    inverse.priors{} - a cell array of anatomical and functional priors to be
%    considered
%
%     inverse.woi    - time window of interest ([start stop] in ms)
%     inverse.lpf    - band-pass filter - low frequency cut-off (Hz)
%     inverse.hpf    - band-pass filter - high frequency cut-off (Hz)
%     inverse.Han    - switch for Hanning window
%     inverse.xyz    - (n x 3) locations of spherical VOIs
%     inverse.rad    - radius (mm) of VOIs
%
%     inverse.Nm     - maximum number of channel modes
%     inverse.Nr     - maximum number of temporal modes
%     inverse.Np     - number of sparse priors per hemisphere
%     inverse.smooth - smoothness of source priors (0 to 1)
%     inverse.Na     - number of most energetic dipoles
%     inverse.sdv    - standard deviations of Gaussian temporal correlation
%     inverse.Qe     - any sensor error components (e.g. empty-room data)
%     inverse.Qe0     - minimum amount of sensor noise power relative to
%                        signal eg 0.1 would correspond to power SNR of 10.0
%     inverse.A       - predefined spatial modes (Nchans*Nmodes) to project
%                       sensor data through
%
% Evaluates:
%
%     inverse.M      - MAP projector (reduced)
%     inverse.J{i}   - Conditional expectation (i conditions) J = M*U*Y
%     inverse.L      - Lead field (reduced UL := U*L)
%     inverse.qC     - spatial covariance
%     inverse.qV     - temporal correlations
%     inverse.T      - temporal projector
%     inverse.U(j)   - spatial projector (j modalities) - derived from data
%     inverse.A      - pre-specified spatial projector
%     inverse.Y{i}   - reduced data (i conditions) UY = UL*J + UE
%     inverse.Is     - Indices of active dipoles
%     inverse.It     - Indices of time bins
%     inverse.Ic{j}  - Indices of good channels (j modalities)
%     inverse.Nd     - number of dipoles
%     inverse.pst    - peristimulus time
%     inverse.dct    - frequency range
%     inverse.F      - log-evidence
%     inverse.VE     - variance explained in spatial/temporal subspaces (%)
%     inverse.R2     - variance in subspaces accounted for by model (%)
%     inverse.scale  - scaling of data for each of j modalities
%__________________________________________________________________________
%
% This version is for single subject single modality analysis and therefore
% contains none of the associated scaling factors.
% No symmetric priors are used in this implementation (just single patches)
% There is an option for a Beamforming prior : inversion type 'EBB'
% also added new beamforming method- using GS rather than ARD- from Juan
% David Martinez Vargas 'EBBgs'
%
% The code was used in
% Lopez, J. D., Penny, W. D., Espinosa, J. J., Barnes, G. R. (2012).
% A general Bayesian treatment for MEG source reconstruction incorporating
% lead field uncertainty.
% Neuroimage 60(2), 1194-1204 doi:10.1016/j.neuroimage.2012.01.077.
%__________________________________________________________________________
 
% Jose David Lopez, Gareth Barnes, Vladimir Litvak
% Copyright (C) 2015-2022 Wellcome Centre for Human Neuroimaging


Nl = length(D);

if Nl>1
    error('function only defined for a single subject');
else
    D=D{1};
end