3 papers
cs.LG2026
SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels
Jingying Ma, Feng Wu, Yucheng Xing +5
Electroencephalography (EEG) foundation models (EFMs) have shown strong potential for transferable representation learning, yet their adaptation in realistic settings remains chall…
eess.SP2026
Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning
Zhisheng Chen, Yingwei Zhang, Qizhen Lan +7
Current foundation models for electroencephalography (EEG) rely on architectures adapted from computer vision or natural language processing, typically treating neural signals as p…
cs.LG2025
ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models
Chenyu Liu, Yuqiu Deng, Tianyu Liu +4
Electroencephalography (EEG), with its broad range of applications, necessitates models that can generalize effectively across various tasks and datasets. Large EEG Models (LEMs) a…