Background: Since the seminal “Attention Is All You Need” paper (2017), transformer architectures have progressively reshaped sequence modelling far beyond natural‑language processing, including the analysis of multichannel electroencephalography (EEG). Objective: This mini‑review synthesizes research from 2017 to 2025 on transformer‑based EEG models, covering emotion recognition, motor‑imagery (MI) brain–computer interfaces, seizure prediction, artifact handling, cognitive regression, and the emerging task of channel reconstruction. Methods: We categorize models by architectural strategy—hybrid CNN–Transformers, pure (end‑to‑end) Transformers, graph and vision Transformers, encoder–decoder reconstruction schemes—and summarize their methodological innovations (multi‑scale convolutions, channel attention, masked‑channel learning, self‑supervised Swin modules). Results: Across benchmarks, transformers consistently outperform prior CNN/LSTM baselines. Representative gains include: ERTNet achieving 74.2% ± 2.6 accuracy on DEAP (valence/arousal) and CTNet reaching 82.5% (IV‑2a, MI 4‑class). Vision‑Transformers surpass CNNs on CHB‑MIT seizure prediction (94.9%), while Swin‑based MST‑Net attains R² = 0.93 for cognitive‑load regression. The light‑weight Auto‑EEG‑Recon (0.08 M parameters) reconstructs masked channels with R² = 0.93. Discussion: Key strengths of transformers include long‑range temporal–spatial modeling, data‑fusion flexibility, and growing interpretability via attention heat‑maps and class‑activation projections. Core challenges remain data scarcity, overfitting, computational cost, and the need for domain‑grounded explainability. Promising remedies involve self‑supervised pre‑training, augmentation, graph priors, efficient attention, and multimodal fusion. Conclusions: Transformers have become pivotal in EEG research, establishing new state‑of‑the‑art results and enabling applications from robust BCIs to edge‑ready noise reconstruction. Continued interdisciplinary efforts and larger EEG corpora are required to unlock their full translational impact in neurotechnology and clinical neurodiagnostics.