Hello,

I am a newbie here, so I apologise in advance if my question has been answered previously. I think I have a similar problem for my connectivity analysis. I am trying to understand how the choice of the scout function and the aggregation procedure would affect MEG connectivity results. I managed to run the ''scoutime' 1 option (before) for PCA and Mean scout functions, over epoched and preprocessed resting state data, but matlab seems not to be able to handle the after scoutime option. I know I could reduce the N of scouts but my ultimate goal is to do whole brain network analysis.

I guess I'd need to simplify my data, by some sort of averging over scouts time series(?), but I wanted to hear your opinion first. Please let me know if I am missing something obvious.

Here is the piece of my code:

```
**% Process: Envelope Correlation NxN [2020]**
sFiles_Atla1_Alpha_AverageAfter = bst_process('CallProcess', 'process_henv1n', sFiles, [], ...
'timewindow', [], ...
'scouts', {'Schaefer_100_17net', {'Background+FreeSurfer_Defined_Medial_Wall L', 'Background+FreeSurfer_Defined_Medial_Wall R', 'ContA_IPS_1 L', 'ContA_IPS_1 R', 'ContA_PFCl_1 L', 'ContA_PFCl_1 R', 'ContA_PFCl_2 L', 'ContA_PFCl_2 R', 'ContB_IPL_1 R', 'ContB_PFCld_1 R', 'ContB_PFClv_1 L', 'ContB_PFClv_1 R', 'ContB_Temp_1 R', 'ContC_Cingp_1 L', 'ContC_Cingp_1 R', 'ContC_pCun_1 L', 'ContC_pCun_1 R', 'ContC_pCun_2 L', 'DefaultA_IPL_1 R', 'DefaultA_PFCd_1 L', 'DefaultA_PFCd_1 R', 'DefaultA_PFCm_1 L', 'DefaultA_PFCm_1 R', 'DefaultA_pCunPCC_1 L', 'DefaultA_pCunPCC_1 R', 'DefaultB_IPL_1 L', 'DefaultB_PFCd_1 L', 'DefaultB_PFCd_1 R', 'DefaultB_PFCl_1 L', 'DefaultB_PFCv_1 L', 'DefaultB_PFCv_1 R', 'DefaultB_PFCv_2 L', 'DefaultB_PFCv_2 R', 'DefaultB_Temp_1 L', 'DefaultB_Temp_2 L', 'DefaultC_PHC_1 L', 'DefaultC_PHC_1 R', 'DefaultC_Rsp_1 L', 'DefaultC_Rsp_1 R', 'DorsAttnA_ParOcc_1 L', 'DorsAttnA_ParOcc_1 R', 'DorsAttnA_SPL_1 L', 'DorsAttnA_SPL_1 R', 'DorsAttnA_TempOcc_1 L', 'DorsAttnA_TempOcc_1 R', 'DorsAttnB_FEF_1 L', 'DorsAttnB_FEF_1 R', 'DorsAttnB_PostC_1 L', 'DorsAttnB_PostC_1 R', 'DorsAttnB_PostC_2 L', 'DorsAttnB_PostC_2 R', 'DorsAttnB_PostC_3 L', 'LimbicA_TempPole_1 L', 'LimbicA_TempPole_1 R', 'LimbicA_TempPole_2 L', 'LimbicB_OFC_1 L', 'LimbicB_OFC_1 R', 'SalVentAttnA_FrMed_1 L', 'SalVentAttnA_FrMed_1 R', 'SalVentAttnA_Ins_1 L', 'SalVentAttnA_Ins_1 R', 'SalVentAttnA_Ins_2 L', 'SalVentAttnA_ParMed_1 L', 'SalVentAttnA_ParMed_1 R', 'SalVentAttnA_ParOper_1 L', 'SalVentAttnA_ParOper_1 R', 'SalVentAttnB_IPL_1 R', 'SalVentAttnB_PFCl_1 L', 'SalVentAttnB_PFCl_1 R', 'SalVentAttnB_PFCmp_1 L', 'SalVentAttnB_PFCmp_1 R', 'SomMotA_1 L', 'SomMotA_1 R', 'SomMotA_2 L', 'SomMotA_2 R', 'SomMotA_3 R', 'SomMotA_4 R', 'SomMotB_Aud_1 L', 'SomMotB_Aud_1 R', 'SomMotB_Cent_1 L', 'SomMotB_Cent_1 R', 'SomMotB_S2_1 L', 'SomMotB_S2_1 R', 'SomMotB_S2_2 L', 'SomMotB_S2_2 R', 'TempPar_1 L', 'TempPar_1 R', 'TempPar_2 R', 'TempPar_3 R', 'VisCent_ExStr_1 L', 'VisCent_ExStr_1 R', 'VisCent_ExStr_2 L', 'VisCent_ExStr_2 R', 'VisCent_ExStr_3 L', 'VisCent_ExStr_3 R', 'VisCent_Striate_1 L', 'VisPeri_ExStrInf_1 L', 'VisPeri_ExStrInf_1 R', 'VisPeri_ExStrSup_1 L', 'VisPeri_ExStrSup_1 R', 'VisPeri_StriCal_1 L', 'VisPeri_StriCal_1 R'}}, ...
'scoutfunc', 1, ... % Mean
'scouttime', 2, ... % After
'removeevoked', 0, ...
'tfmeasure', 'hilbert', ... % Hilbert transform
'edit', struct(...
'Comment', 'Test', ...
'TimeBands', [], ...
'Freqs', {{'alpha', '8, 12', 'mean'}}, ...
'ClusterFuncTime', 'none', ...
'Measure', 'none', ...
'Output', 'all', ...
'RemoveEvoked', 0, ...
'SaveKernel', 0), ...
'tfsplit', 1, ...
'cohmeasure', 'oenv', ... % Envelope correlation (orthogonalized)
'statdyn', 'static', ... % Static
'win_length', 1.5, ...
'win_overlap', 50, ...
'parallel', 0, ...
'outputmode', 2);
```

and the matlab error: "Requested 900x225060004 (3018.3GB) array exceeds maximum array size preference (125.6GB). This might cause matalab to become unresposive."

Many thanks in advance.

D.