Variational Bayes estimation for MEG.
Estimate current variance by using time sequence of all trials.
Prune algorithm.
-- Syntax
[Model,Info] = vbmeg_ard_estimate2(B, Gall, Gact, COV, vb_parm),
[Model,Info] = vbmeg_ard_estimate2(B, Gall, Gact, COV, vb_parm, Modelinit)
-- Input
B{Nsession} : MEG magnetic field data matrix
size(B{i}) = [Nsensors(i), Tsample, Ntrials(i)]
Gall{Nsession}: Lead field for whole brain region as background activity
size(Gall{i}) = [Nsensors(i) Ndipole]
If Gall = [], the sparse-mode (focal dipoles only) is
executed.
Gact{Nsession} : Lead field (Current basis function)
for region with high estimation accuracy
size(Gact{i}) = [Nsensors(i) Ndipole]
If Gact = [], the iso-mode (no focal dipoles) is
executed.
COV{Nsession} : Noise covariance matrix
size(COV{i}) = [Nsensors(i) Nsensors(i)]
vb_parm : Constants parameter
-- Optional input
Modelinit : Initial Model parameters, if given.
if not given, the variables vb_parm.a0 and vb_parm.v0 are used.
The size of each variable in 'Modelinit' must be the same as that of
the output 'Model'.
-- Output
Model.a : Current variance (1/alpha)
Model.v : Current variance for background activity
Model.sx : Observation noise variance
Info : Containing convergence information such as 'FE','Ev','Err'
-- Note
Brain noise : Background brain activity is assumed
as independent Gaussian noise with same variance
"Nvact" (# of vertex for active dipoles) and
"Nt" (length of time window) are common for all sessions.
"Nsensors" (# of sensor channels in a session) and
"Ntrial" (# of trials in a session) could depend on each session.
Note that ax0_j, ax0_z, and sx are defined as variance:
the inverse of inverse-variance (i.e having the diminsion of variance).
These notations are different from those in Sato-san's note.
2004-12-5 M. Sato
2005-04-12 O.Yamashita
* The whole program are revised
so that variables have consistent names with Sato-san's note.
* The noise variance, sx, ax_z and ax_j are independently handled.
* A field 'fix_parm' is added to 'vb_parm' struct.
* The input 'COV' must be given.
2005-04-16 M. Sato
* Modified free-energy calculation
2005-05-07 M. Sato
* Modified variance update (Faster convergence)
2005-05-13 O.Yamashita
* 'Modelinit' is added as the 6th input optional argument.
* Iso-mode is implemented. (when Gact is [])
* A field 'fix_parm' is changed to two flag paramters, 'update_v' and
'update_sx'.
* lower bound of 'gain_z'
2005-05-16 O.Yamashita
* Sparse-mode is implementd. (when Gall is [])
* lower bound of 'gain_j'
2005-05-31 O.Yamashita
* constant term of free energy
2005-06-02 O.Yamashita
* constat term of free energy revised
2005/08/22 O.Yamashita --- ver.30b
* vb_parm is introduced
* Output argument is modified
* Minor bug fix
2005/09/29 O. Yamashita
* Minor bug fix (when Norinet=2 & iso-mode)
2006/04/16 M. Sato
* vb_parm.Npre_train is introduced
VB-update equation is used for iteration <= Npre_train
2006/08/20 M. Sato
* Soft normal constraint
2008/06/20 M. Sato
* Only support focal model with normal current direction
* Do not support soft normal constraint
* prune operation is omitted since no pruning is done in Ta0>=10
2012-02-08 taku-y
[debug] Fixed so that flag 'update_sx' correctly works when put the
flag 'false'.
------------ Relative variance calculation ----------------
In this implementation,
Current variance 'ax_z' is defined as ratio w.r.t sx :
ax_z * sx = < Z*Z>
Copyright (C) 2011, ATR All Rights Reserved.
License : New BSD License(see VBMEG_LICENSE.txt)0001 function [Model,Info] = ... 0002 vbmeg_ard_estimate4(B, Gall, Gact, COV, vb_parm, Modelinit) 0003 % Variational Bayes estimation for MEG. 0004 % Estimate current variance by using time sequence of all trials. 0005 % Prune algorithm. 0006 % 0007 % -- Syntax 0008 % [Model,Info] = vbmeg_ard_estimate2(B, Gall, Gact, COV, vb_parm), 0009 % [Model,Info] = vbmeg_ard_estimate2(B, Gall, Gact, COV, vb_parm, Modelinit) 0010 % 0011 % -- Input 0012 % B{Nsession} : MEG magnetic field data matrix 0013 % size(B{i}) = [Nsensors(i), Tsample, Ntrials(i)] 0014 % Gall{Nsession}: Lead field for whole brain region as background activity 0015 % size(Gall{i}) = [Nsensors(i) Ndipole] 0016 % If Gall = [], the sparse-mode (focal dipoles only) is 0017 % executed. 0018 % Gact{Nsession} : Lead field (Current basis function) 0019 % for region with high estimation accuracy 0020 % size(Gact{i}) = [Nsensors(i) Ndipole] 0021 % If Gact = [], the iso-mode (no focal dipoles) is 0022 % executed. 0023 % COV{Nsession} : Noise covariance matrix 0024 % size(COV{i}) = [Nsensors(i) Nsensors(i)] 0025 % vb_parm : Constants parameter 0026 % 0027 % -- Optional input 0028 % Modelinit : Initial Model parameters, if given. 0029 % if not given, the variables vb_parm.a0 and vb_parm.v0 are used. 0030 % The size of each variable in 'Modelinit' must be the same as that of 0031 % the output 'Model'. 0032 % 0033 % -- Output 0034 % Model.a : Current variance (1/alpha) 0035 % Model.v : Current variance for background activity 0036 % Model.sx : Observation noise variance 0037 % Info : Containing convergence information such as 'FE','Ev','Err' 0038 % 0039 % -- Note 0040 % Brain noise : Background brain activity is assumed 0041 % as independent Gaussian noise with same variance 0042 % 0043 % "Nvact" (# of vertex for active dipoles) and 0044 % "Nt" (length of time window) are common for all sessions. 0045 % 0046 % "Nsensors" (# of sensor channels in a session) and 0047 % "Ntrial" (# of trials in a session) could depend on each session. 0048 % 0049 % Note that ax0_j, ax0_z, and sx are defined as variance: 0050 % the inverse of inverse-variance (i.e having the diminsion of variance). 0051 % These notations are different from those in Sato-san's note. 0052 % 0053 % 2004-12-5 M. Sato 0054 % 2005-04-12 O.Yamashita 0055 % * The whole program are revised 0056 % so that variables have consistent names with Sato-san's note. 0057 % * The noise variance, sx, ax_z and ax_j are independently handled. 0058 % * A field 'fix_parm' is added to 'vb_parm' struct. 0059 % * The input 'COV' must be given. 0060 % 2005-04-16 M. Sato 0061 % * Modified free-energy calculation 0062 % 2005-05-07 M. Sato 0063 % * Modified variance update (Faster convergence) 0064 % 2005-05-13 O.Yamashita 0065 % * 'Modelinit' is added as the 6th input optional argument. 0066 % * Iso-mode is implemented. (when Gact is []) 0067 % * A field 'fix_parm' is changed to two flag paramters, 'update_v' and 0068 % 'update_sx'. 0069 % * lower bound of 'gain_z' 0070 % 2005-05-16 O.Yamashita 0071 % * Sparse-mode is implementd. (when Gall is []) 0072 % * lower bound of 'gain_j' 0073 % 2005-05-31 O.Yamashita 0074 % * constant term of free energy 0075 % 2005-06-02 O.Yamashita 0076 % * constat term of free energy revised 0077 % 2005/08/22 O.Yamashita --- ver.30b 0078 % * vb_parm is introduced 0079 % * Output argument is modified 0080 % * Minor bug fix 0081 % 2005/09/29 O. Yamashita 0082 % * Minor bug fix (when Norinet=2 & iso-mode) 0083 % 2006/04/16 M. Sato 0084 % * vb_parm.Npre_train is introduced 0085 % VB-update equation is used for iteration <= Npre_train 0086 % 2006/08/20 M. Sato 0087 % * Soft normal constraint 0088 % 2008/06/20 M. Sato 0089 % * Only support focal model with normal current direction 0090 % * Do not support soft normal constraint 0091 % * prune operation is omitted since no pruning is done in Ta0>=10 0092 % 2012-02-08 taku-y 0093 % [debug] Fixed so that flag 'update_sx' correctly works when put the 0094 % flag 'false'. 0095 % 0096 % ------------ Relative variance calculation ---------------- 0097 % In this implementation, 0098 % Current variance 'ax_z' is defined as ratio w.r.t sx : 0099 % ax_z * sx = < Z*Z> 0100 % 0101 % Copyright (C) 2011, ATR All Rights Reserved. 0102 % License : New BSD License(see VBMEG_LICENSE.txt) 0103 0104 global flag_pinv; 0105 if isempty(flag_pinv), flag_pinv = false; end 0106 0107 Ntrain = vb_parm.Ntrain; 0108 Nskip = vb_parm.Nskip; 0109 a_min = vb_parm.a_min; 0110 a_max = vb_parm.a_max; 0111 0112 if isfield(vb_parm, 'Npre_train') 0113 % Number of iteration using original VB-update rule 0114 % for stable estimation 0115 Npre_train = vb_parm.Npre_train; 0116 else 0117 Npre_train = Ntrain; 0118 end 0119 0120 fprintf('\n--- New VBMEG estimation program ---\n\n') 0121 fprintf('--- Initial VB-update iteration = %d\n',Npre_train) 0122 fprintf('--- Total update iteration = %d\n',Ntrain) 0123 0124 % Number parameters 0125 Ntrials = vb_parm.Ntrials; % Number of trials 0126 Nsensors = vb_parm.Nsensors; % Number of sensors 0127 Nsession = vb_parm.Nsession; % Number of sessions 0128 Nwindow = vb_parm.Nwindow; % Number of time windows 0129 Twindow = vb_parm.Twindow; % Time window index [Tstart, Tend] 0130 Ntotaltrials = sum(Ntrials); % Total number of trials in all sessions 0131 Nerror = sum(Ntrials.*Nsensors); % Total MEG channel used for error estimate 0132 0133 Nvact = vb_parm.Nvact; % Number of active current vertices 0134 Njact = vb_parm.Njact; % Number of active dipoles parameters 0135 Njall = vb_parm.Njall; % Number of whole brain vertices (dipole) 0136 % for background activity 0137 Norient = vb_parm.Norient; % Number of current orientation component 0138 Norient_var = vb_parm.Norient_var;% Number of current orientation component 0139 % for variance estimation 0140 Nvaract = Nvact * Norient_var;% Number of variance parameters 0141 % (=length(ax_z)) 0142 Ratio = Norient/Norient_var; % = (# of orientation) 0143 % /(# of orientation in variance) 0144 0145 % Set prior parameters 0146 Ta0 = vb_parm.Ta0 ; % confidence parameter of focal 0147 sx0 = vb_parm.sx0; % observation noise variance 0148 a0 = max(vb_parm.a0, a_min ); % initial variance of focal 0149 0150 if exist('Modelinit','var') & ~isempty(Modelinit) 0151 a0s = max(Modelinit.a, a_min ); 0152 else 0153 a0s = repmat(a0, [1 Nwindow]); 0154 end 0155 0156 if isempty(Gact) 0157 error('No leadlield is given') 0158 end 0159 0160 % Flag cotroling updates of variance parameters 0161 update_sx = 1; 0162 if isfield(vb_parm, 'update_sx') 0163 update_sx = vb_parm.update_sx; 0164 end 0165 0166 %%% calculate BB' and GG' 0167 for ns = 1 : Nsession 0168 for nw = 1 : Nwindow 0169 B0 = 0; 0170 for i = 1 : Ntrials(ns) 0171 Bt = B{ns}(:,Twindow(nw,1):Twindow(nw,2),i); 0172 B0 = B0 + Bt * Bt'; 0173 end 0174 BBall{ns,nw} = B0; 0175 end 0176 end 0177 0178 clear B Gall 0179 0180 % Temporal variable for variance (ratio) 0181 aall = zeros(Nvaract,Nwindow); % Modified by TY 2005-06-15 0182 sxall = zeros(Nwindow,1); 0183 0184 % Free energy and Error history 0185 Info = zeros(Nwindow*Ntrain, 6); 0186 k_check = 0; 0187 0188 %%%%% Time window loop %%%%%%%% 0189 for nw=1:Nwindow 0190 0191 Nt = Twindow(nw,2) - Twindow(nw,1) + 1; 0192 Ntt = Nt*Ntotaltrials; 0193 0194 % confidence parameters 0195 gx0_z = Ta0; 0196 gx_z = 0.5 * Ntt * Ratio + gx0_z; 0197 0198 % initial variance parameters 0199 ax0_z = a0s(:,nw) ; 0200 0201 % variance hyper-parameter initialization 0202 if vb_parm.cont_pr == OFF | nw == 1 0203 % use the prior variance as the initial expected value 0204 ax_z = ax0_z; 0205 sx = sx0; 0206 else 0207 % use the estimated variance in the previous time window as the 0208 % initial expected value 0209 ix_zero = find(ax_z < a_min); % indices taking small variance 0210 ax_z(ix_zero) = ax0_z(ix_zero); 0211 end 0212 0213 %%%%%% Estimation Loop %%%%%% 0214 for k=1:Ntrain 0215 %%% VB E-step -- current estimation 0216 0217 % Initialize averaging variable 0218 tr1_b = 0; % (B - G*J)^2 0219 mean_zz = zeros(Nvaract,1); % diag( Z*Z ) 0220 gain_z = zeros(Nvaract,1); % Estimation Gain for Z 0221 Hj = 0; % log(det(SB)) 0222 0223 for ns=1:Nsession 0224 Ntry = Ntrials(ns); 0225 0226 % Lead field for each session 0227 G = Gact{ns}; 0228 Gt = G'; 0229 0230 % MEG covariance for each session 0231 BB = BBall{ns,nw}; % (B * B') 0232 0233 % Noise covariance for each session 0234 Cov = COV{ns}; % Sigma_G^{-1} 0235 0236 if Ratio == 1 0237 A_z = ax_z; 0238 else 0239 A_z = repmat(ax_z,Ratio,1) 0240 end 0241 0242 % Inverse filter for each session 0243 % GSG = G*(A_z.*Gt) ; % Ga * alpha *Ga' 0244 GSG = G * vb_repmultiply(Gt, A_z) ; 0245 SB = GSG + Cov; % Sigma_B 0246 if flag_pinv, SB_inv = pinv( SB ); % Sigam_B^{-1} 0247 else SB_inv = inv(SB); end 0248 GSB = Gt*SB_inv; % Ga' * Sigma_B^{-1} 0249 SBBS = SB_inv*BB*SB_inv; % Sigma_B^{-1}*BB*Sigma_B^{-1} 0250 0251 % Reconstraction error = sum( (B-G*J)'*Cov*(B-G*J) ) 0252 tr1_b = tr1_b + sum(sum(Cov.*SBBS)); 0253 0254 % Current magnitude = diag( sum( Z * Z ) ) 0255 mean_zz = mean_zz + ax_z ... 0256 .* sum(reshape(sum((GSB*BB).*GSB,2),[Nvaract Ratio]),2); 0257 % Estimation gain for Z = diag(ax_z*Gt*SB_inv*G) 0258 gain_z = gain_z + Nt*Ntry*ax_z ... 0259 .* sum(reshape(sum(Gt.*GSB, 2),[Nvaract Ratio]), 2); 0260 0261 Hj = Hj - 0.5*Nt*Ntry*vb_log_det(SB); 0262 end; 0263 0264 % Noise variance estimation 0265 sx_total = (tr1_b + sum(mean_zz)); 0266 if update_sx, 0267 sx = sx_total/(Nt*Nerror); 0268 end 0269 0270 %%%%% VB M-step -- variance update 0271 0272 % Save old value for Free energy calculation 0273 ax_z_old = ax_z; 0274 0275 % --- Active current variance estimation 0276 % 0277 % VB update equation 0278 % gx_z * ax_z = ... 0279 % (0.5*Ntt*Ratio + gx0_z) * ax_z = ... 0280 % mean_zz_all + 0.5*ax_z.*( Ntt * Ratio - gain_z ); 0281 0282 mean_zz_all = 0.5*ax_z.*mean_zz/sx + gx0_z.*ax0_z; 0283 0284 % Update the current variance parameters 0285 if k <= Npre_train 0286 % --- VB update rule 0287 % Active current variance estimation 0288 ax_z_total = mean_zz_all + 0.5*ax_z.*(Ntt*Ratio - gain_z); 0289 ax_z = ax_z_total ./ gx_z; 0290 else 0291 % --- Faster convergence update rule 0292 % Active current variance estimation 0293 gain_z = max(gain_z, eps); 0294 ax_z = mean_zz_all ./ (0.5*gain_z + gx0_z); 0295 end 0296 0297 %%%%%%%% Free Energy Calculation %%%%%%%% 0298 % Model complexity for current variance 0299 rz = ax0_z./ax_z_old; 0300 Haz = sum( gx0_z.*(log(rz) - rz + 1)); 0301 0302 LP = - 0.5*Nt*Nerror*log(sx); 0303 0304 % Data Likelihood & Free Energy 0305 Ea = - 0.5*( sx_total/sx - Nt*Nerror ); 0306 FE = LP + Hj + Ea + Haz ; 0307 Ev = LP + Hj; % evidence (without constants) 0308 Err = tr1_b/sum(sum(BB));% Normalized Error 0309 0310 % DEBUG Info 0311 k_check = k_check + 1; 0312 Info(k_check,:) = [... 0313 FE, Ev, Hj, LP, Err, cond(SB)]; 0314 % end of free energy calculation 0315 0316 if mod(k, Nskip)==0 || k==Ntrain 0317 fprintf('Tn=%3d, Iter=%4d, FE=%f, Error=%e\n', ... 0318 nw, k, FE, Err); 0319 end; 0320 0321 end % end of learning loop 0322 0323 aall(:,nw) = ax_z; 0324 sxall(nw) = sx; 0325 end % end of time window loop 0326 0327 Model.a = aall ; % Current variance 0328 Model.sx = sxall; % Sensor noise variance 0329 Model.v = zeros(Nwindow,1); 0330 0331 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 0332 %%% functions specific for this function 0333 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 0334 function FEconst = free_energy_constant(gx_j, gx0_j, gx_z, gx0_z,... 0335 Nt, Ntotaltrials, Nerror, Njall) 0336 0337 gx_sx = 0.5*Nt*Nerror; 0338 L0 = 0.5*Nt*(Njall*Ntotaltrials*qG(gx_j) + Ntotaltrials*sum(qG(gx_z)) + Nerror*qG(gx_sx) - Nerror); 0339 H0 = HG0(gx_sx,0) + HG0(gx_j,gx0_j)+sum(HG0(gx_z, gx0_z)); 0340 FEconst = L0+H0; 0341 0342 %%%%% 0343 function y = qG(gx) 0344 N=length(gx); 0345 nz = find(gx ~= 0); % non-zero components 0346 y = zeros(N,1); 0347 y_tmp = psi(gx(nz)) - log(gx(nz)); 0348 y(nz) = y_tmp; 0349 0350 %%%%%% 0351 function y =HG0(gx, gx0) 0352 N=length(gx0); 0353 nz = find(gx ~= 0); % non-zero components 0354 nz0 = find(gx0 ~= 0); % non-zero components 0355 % gammma 0356 y1 = zeros(N,1); 0357 y1_tmp = gammaln(gx(nz)) - gx(nz).*psi(gx(nz)) + gx(nz); 0358 y1(nz) = y1_tmp; 0359 % gamma0 0360 y2 = zeros(N,1); 0361 y2_tmp = gammaln(gx0(nz0)) - gx0(nz0).*log(gx0(nz0)) + gx0(nz0); 0362 y2(nz0) = y2_tmp; 0363 % gamma*gamma0 0364 y3 = zeros(N,1); 0365 y3_tmp = gx0(nz0).*(psi(gx(nz0))-log(gx(nz0))); 0366 y3(nz0) = y3_tmp; 0367 0368 y = y1 - y2 + y3; 0369 0370 0371 0372