META-EEG: Meta-learning-based class-relevant EEG representation learning for zero-calibration brain–computer interfaces

Han, Ji-Wung; Bak, Soyeon; Kim, Jun-Mo; Choi, WooHyeok; Shin, Dong-Hee; Son, Young-Han; Kam, Tae-Eui

doi:10.1016/j.eswa.2023.121986

Cited by 4 publications

(1 citation statement)

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“…Moreover, Han et al ( 2024 ) proposed META-EEG, an advanced implicit transfer learning framework designed to tackle inter-subject variability in MI-BCIs. By incorporating gradient-based meta-learning with an intermittent freezing strategy, META-EEG ensures efficient feature representation learning, providing a robust zero-calibration solution.…”

Section: Resultsmentioning

confidence: 99%

Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions

Ahuja,

Sethia

2024

Front. Hum. Neurosci.

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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.

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Section: Resultsmentioning

confidence: 99%