[2509.24222] Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning

Electrical Engineering and Systems Science > Signal Processing arXiv:2509.24222 (eess) [Submitted on 29 Sep 2025 (v1), last revised 3 Mar 2026 (this version, v2)] Title:Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning Authors:Zhisheng Chen, Yingwei Zhang, Qizhen Lan, Tianyu Liu, Huacan Wang, Yi Ding, Ziyu Jia, Ronghao Chen, Kun Wang, Xinliang Zhou View a PDF of the paper titled Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning, by Zhisheng Chen and 9 other authors View PDF Abstract:Current foundation models for electroencephalography (EEG) rely on architectures adapted from computer vision or natural language processing, typically treating neural signals as pixel grids or token sequences. This approach overlooks that the neural activity is activated by diverse sparse coding across a complex geometric topological cortex. Inspired by biological neural mechanisms, we propose the Unified Neural Topological Foundation Model (Uni-NTFM), an architecture rooted in three core neuroscience principles. In detail, to align with the brain's decoupled coding mechanism, we design the Heterogeneous Feature Projection Module. This module simultaneously encodes both time-domain non-stationary transients and frequency-domain steady-state rhythms, ensuring high quality in both waveform morphology and spectral rhythms. Moreover, we introduce a Topological Embedding mechanism to inject structured spatial priors and align different sensor conf...