本文进行了系统的文献综述,提供数据增强(DA)的全面分类,迁移学习(TL),以及在少发学习(FSL)的背景下进行EEG信号分类的自我监督学习(SSL)技术。EEG信号在各种范例中显示出巨大的潜力,包括运动图像,情感识别,视觉诱发电位,稳态视觉诱发电位,快速串行视觉演示,事件相关电位,和精神负荷。然而,挑战,如有限的标记数据,噪音,以及学科间/学科内的变异性阻碍了传统机器学习(ML)和深度学习(DL)模型的有效性。这篇综述有条不紊地探讨了FSL是如何处理的,合并DA,TL,和SSL,可以解决这些挑战并增强特定EEG范例中的分类性能。它还深入研究了与EEG信号分类中这些技术相关的开放研究挑战。具体来说,这篇综述审查了针对各种脑电图范式定制的DA策略的识别,创建有效知识转移的TL架构,以及从EEG数据中进行无监督表示学习的SSL方法的制定。解决这些挑战对于增强基于FSL的EEG信号分类的功效和鲁棒性至关重要。通过介绍FSL技术的结构化分类并讨论相关的研究挑战,本系统综述为未来的EEG信号分类研究提供了有价值的见解.这些发现旨在指导和激励研究人员,促进在现实环境中应用FSL方法以改进EEG信号分析和分类的进步。
This paper presents a systematic literature review, providing a comprehensive taxonomy of Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) techniques within the context of Few-Shot Learning (FSL) for EEG signal classification. EEG signals have shown significant potential in various paradigms, including Motor Imagery, Emotion Recognition, Visual Evoked Potentials, Steady-State Visually Evoked Potentials, Rapid Serial Visual Presentation, Event-Related Potentials, and Mental Workload. However, challenges such as limited labeled data, noise, and inter/intra-subject variability have impeded the effectiveness of traditional machine learning (ML) and deep learning (DL) models. This review methodically explores how FSL approaches, incorporating DA, TL, and SSL, can address these challenges and enhance classification performance in specific EEG paradigms. It also delves into the open research challenges related to these techniques in EEG signal classification. Specifically, the review examines the identification of DA strategies tailored to various EEG paradigms, the creation of TL architectures for efficient knowledge transfer, and the formulation of SSL methods for unsupervised representation learning from EEG data. Addressing these challenges is crucial for enhancing the efficacy and robustness of FSL-based EEG signal classification. By presenting a structured taxonomy of FSL techniques and discussing the associated research challenges, this systematic review offers valuable insights for future investigations in EEG signal classification. The findings aim to guide and inspire researchers, promoting advancements in applying FSL methodologies for improved EEG signal analysis and classification in real-world settings.