Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are two of the most prevalent neurodegenerative disorders, yet their overlapping clinical presentations pose substantial diagnostic challenges. Current diagnostic methods rely on costly neuroimaging and subjective clinical assessments, creating an urgent need for accessible, objective screening tools. This paper presents a novel approach using a Bidirectional Temporal Graph Convolutional Network (GCN)-Transformer framework that incorporates comprehensive fractal pattern analysis to automatically classify AD, FTD, and cognitively normal (CN) individuals from EEG signals. 14 different features from each EEG channel were extracted to build a complete picture: 3 time domain features (mean, variance, standard deviation), 4 fractal dimension measures (Higuchi, Petrosian, Katz Fractal Dimensions, and Detrended Fluctuation Analysis), and 7 frequency domain features (spectral entropy, band powers across delta,theta, alpha, beta, gamma bands, and peak frequency). The aim of these multi-scale features is to effectively capture the self-similar patterns and nonlinear dynamics characteristic of neurodegenerative brain activity across all 19 EEG channels. The architecture combines graph convolutional layers with graph constrained multi-head attention mechanisms operating on dynamic temporal adjacency matrices, with bidirectional processing strengthened by positional encoding. Ten-fold cross-validation of the dataset yielded training accuracy of 98.65±0.38% and test accuracy of 97.00±0.45%, while leave-one-subject-out validation achieved 85.00% accuracy along with 86.23% precision, 85.00% recall, 85.34% F1-score, 85.00% sensitivity, and 92.50% specificity. Our ablation studies demonstrated clear advantages over standard Graphical Convolutional Network (94.12±0.78%) and Graphical Attention Transformer variants (95.45±0.68%).