HarMNqEEG

HarMNqEEG: provide the MATLAB code for the evaluation of batch harmonized z-scores based on the multinational norms.

Descriptive parameter surface (DPs):

Z socre values:

Installation

1. Extract the ZIP file (or clone the git repository) somewhere you can easily reach it.
2. Add the HarMNqEEG folder to your path in MATLAB: e.g. a. using the "setup" dialogue in MATLAB; b. by running the addpath function from your command window or startup script. Note: this package requires Matlab R2021a or latter since the gridded norm from non-uniform samples utilizes griddedInterpolant to interpolate multiple sets of values on the same grid for the incoming sample.

Usage

Folder main contains the main process code including:

Generate corss-spectrum tensor use the code data_gatherer.m [1].

The data_gatherer.m and one example named generate_cross_spectrum.m in the external folder.

notes:

1. check the electrodes name and electrodes order used in data_gatherer.m
2. names shoule be encrypted before generate the corss-spectrum tensor.

step 0: generate metadata table based on the calcuated cross-spectrum tensor.

input: corss-spectrum tensor path output: metadata table Ⅰ

you can chekc the example with: .\data\example\DataInfo_Barbados1978Malnutrition_44.csv

step 1: run step1_preprocess_<typeDPs>.m to get the DPs

input: metadata table Ⅰ
output: DPs table Ⅱ which including log-spectrum /Riemannian vectorized corss-spectrum DPs +meta data

step 2: run step2_harmonize_<typeDPs>.m to get the global z-scores, batch harmonized z-scores and batch harmonized DPs

input: DPs table Ⅱ+{ study name, batch correction reference study name}(the reference batch study name as below)
output: z-score table Ⅲ which including z-scores (global z-scores) and cz-scores (batch corrected z-scores)

step 3: run step3_visualize_<typeDPs>.m to visualize the scatter plot of z-scores

input: z-score table Ⅲ
output: z-score scatter plot

Note:

1. <typeDPs> including traditional log-spectrum DPs (log) and Hermitian Riemannian DPs (riemlogm).

2. In step 2, need to select one closed study for calculating batch harmonized z-scores and DPs. The name of existed batch reference are:

   'ANTNeuro Malaysia' 'BrainAmpDC Chengdu' 'BrainAmpMRplus Chongqing' 'BrainAmpMRplus Germany' 'DEDAAS Barbados1978' 'DEDAAS NewYork' 'EGI Zurich' 'Medicid-3M Cuba1990' 'Medicid-4 Cuba2003' 'Medicid-5 CHBMP' 'NihonKohden Bern' 'actiChamp Russia' 'neuroscan Colombia' 'nvx136 Russia'

Example Data Description

'ANTNeuro Malaysia' 'BrainAmpDC Chengdu' 'BrainAmpMRplus Chongqing' 'BrainAmpMRplus Germany' 'DEDAAS Barbados1978' 'DEDAAS NewYork' 'EGI Zurich' 'Medicid-3M Cuba1990' 'Medicid-4 Cuba2003' 'Medicid-5 CHBMP' 'NihonKohden Bern' 'actiChamp Russia' 'neuroscan Colombia' 'nvx136 Russia'

The data is available here, extract data folder to the root of the repository. The data include:

1. example: a. BarbadosMalnutrition contains the cross-spectrum tensor of Barbados 1978 malnutrition dataset [2] which got by running the data_gatherer.m Note: take care with the EEG epoch which will use in step2 for Hermitian positive defined (HPD) matrix regularization[3]. the path of the cross-spectrum should include the path and name of .mat file which will be used for load data in step 2.
2. norm: Including norms norm_<typeDPs><model><batch>.mat for calculating the global z-scores and batch harmonized z-scores for <typeDPs> and the geometric mean for mapping the cross-spectrum tensor to tangent vector space in step and this only need for Hermitian Riemannian DPs Preprocessing.

Remarks

1. HarMNqEEG norms including 11 studies, see in the paper (HarMNqEEG). We now only support these 11 studies and when running step 2, choose the closed one for harmonization. In future work, we will provide the function for estimating the batch norms of coming to DPs.
2. Octave is not fully supported yet.

[1] Github location of the script: https://github.com/CCC-members/BC-V_group_stat/blob/master/data_gatherer.m

[2] Bringas Vega, M.L., Guo, Y., Tang, Q., Razzaq, F.A., Calzada Reyes, A., Ren, P., Paz Linares, D., Galan Garcia, L., Rabinowitz, A.G., Galler, J.R., Bosch-Bayard, J., Valdes Sosa, P.A., 2019. An Age-Adjusted EEG Source Classifier Accurately Detects School-Aged Barbadian Children That Had Protein Energy Malnutrition in the First Year of Life. Front. Neurosci. 13, 1222. https://doi.org/10.3389/fnins.2019.01222

[3] Schneider-Luftman, D., Walden, A.T., 2016. Partial Coherence Estimation via Spectral Matrix Shrinkage under Quadratic Loss. IEEE Trans. Signal Process. 64, 5767–5777. https://doi.org/10.1109/TSP.2016.2582464

Author: Ying Wang, Min Li, Pedro Antonio Valdés-Sosa.

Create Time: 2021

Copyright(c): 2020-2022 Ying Wang, [email protected]; Min Li, [email protected]; Pedro Antonio Valdés-Sosa [email protected]

Joint China-Cuba LAB, UESTC, Chengdu, China.