Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) represent formidable neurodegenerative challenges. Existing research into optimal feature-extraction techniques for discerning pertinent AD/FTD biomarkers from Electroencephalography (EEG) data presents room for enhancement. Addressing this, our study undertakes a comprehensive evaluation of diverse feature-extraction methodologies, encompassing Higuchi’s Fractal Dimension, Singular Value Decomposition Entropy, Zero Crossing Rate, Detrended Fluctuation Analysis, and Hjorth parameters. Our results highlight SVD Entropy as the superior measure, a finding not deeply explored in previous research with respect to AD/FTD. Furthermore, we aim to design an optimal machine learning pipeline incorporating sliding window segmentation, feature-extraction, and a supervised learning algorithm for discriminating AD/FTD patients from healthy controls. To amplify the interpretability of this study, we harnessed Explainable AI to generate feature-importance topographic brain plots. These plots emphasized the midbrain region’s pronounced feature importance in Parkinson’s Disease detection, which is consistent with current research, underscoring its efficacy in accurately discerning spatial information from EEG. The final results highlight a model incorporating SVD Entropy, KNN Classifier, and 90% overlapped sliding windows as the best performer, yielding a mean F1-score of 93% with an Accuracy of 91% for AD and HC discrimination, an average F1-score of 92.5% with an Accuracy of 93% for FTD and HC differentiation, and F1-score of 91.5% with an Accuracy of 91% for FTD and AD distinction. This research presents a novel architectural pipeline for detecting AD and FTD from EEG data, addressing the crucial need for accurate differentiation between AD and FTD, especially in the early stages where misdiagnosis is common, while also providing a comparative analysis of various feature-extraction measures.