Multicentric initiatives based on high-density electroencephalography (hd-EEG) are urgently needed for the classification and characterization of disease subtypes in diverse and low-resource settings. These initiatives are challenging, with sources of variability arising from differing data acquisition and harmonization methods, multiple preprocessing pipelines, and different theoretical modes and methods to compute source space/scalp functional connectivity. Our team developed a novel pipeline aimed at the harmonization of hd-EEG datasets and dementia classification. This pipeline handles data from recording to machine learning classification based on multi-metric measures of source space connectivity. A user interface is provided for those with limited background in MATLAB. Here, we present our pipeline and provide a detailed a comprehensive step-by-step example for analysts to review the five main stages of the pipeline: data preprocessing, normalization, source transformation, connectivity metrics, and dementia classification. This detailed step-by-step pipeline may improve the assessment of heterogenous, multicentric, and multi-method approaches to functional connectivity in aging and dementia.