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@article{205060,
        author = {Manoj Kumar Sah and Md Iftekhar Ahmad and Faiz Akram and Anurag Bharti},
        title = {Advances in Few-Shot Deep Learning for EEG-Based Epileptic Seizure Analysis: A Systematic Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5328-5351},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205060},
        abstract = {Epilepsy is a common brain issue, and electroencephalography (EEG) remains the key method for checking seizures. Although traditional deep learning models do well in interpreting EEG automatically, they require huge, detailed labeled datasets. The clinics often lack large datasets because seizures aren’t super common and differ from person to person. This systematic review explores the recent advances in few-shot deep learning for seizure detection using EEG. Following PRISMA guidelines, we gathered studies that did original work in applying few-shot deep learning to detect, predict, or classify seizures. From our findings, it’s clear that this area is rapidly evolving. Most researchers rely on metric learning approaches like prototypical and Siamese networks, along with meta-learning strategies. To boost tiny training sets, techniques like data augmentation via GANs and frequency-domain transformations are commonly used. Generally, few-shot methods either match or outperform conventional deep learning when it comes to generalizing across patients. When working with less than ten training examples per patient, these methods show promise, meeting clinical standards of effectiveness. However, there's quite a bit of inconsistency in study evaluations, and testing on varied, multicenter clinical data remains limited. Moreover, real-time applications on edge devices and making outcomes more interpretable haven't been thoroughly examined yet. Overall, few-shot deep learning shows massive potential in overcoming data limitations in epileptic EEG analysis. Moving forward, creating standard benchmarks, conducting prospective clinical trials, and integrating these methods with novel tech for actual clinic use should be top priorities.},
        keywords = {Epileptic Seizure, Few-Shot Learning, Deep Learning, electroencephalography (EEG), Review.},
        month = {June},
        }