BrainPro: Towards Large-scale Brain State-aware EEG Representation Learning

• URL: http://arxiv.org/abs/2509.22050v1
• Date: Fri, 26 Sep 2025 08:34:04 GMT
• Title: BrainPro: Towards Large-scale Brain State-aware EEG Representation Learning
• Authors: Yi Ding, Muyun Jiang, Weibang Jiang, Shuailei Zhang, Xinliang Zhou, Chenyu Liu, Shanglin Li, Yong Li, Cuntai Guan,
• Abstract summary: Recent EEG foundation models have shown improved performance and generalizability over traditional decoding methods.<n>Existing models often fail to explicitly capture channel-to-channel and region-to-region interactions.<n>BrainPro achieves state-of-the-art performance and robust generalization across nine public BCI datasets.
• Score: 34.50028362571382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electroencephalography (EEG) is a non-invasive technique for recording brain electrical activity, widely used in brain-computer interface (BCI) and healthcare. Recent EEG foundation models trained on large-scale datasets have shown improved performance and generalizability over traditional decoding methods, yet significant challenges remain. Existing models often fail to explicitly capture channel-to-channel and region-to-region interactions, which are critical sources of information inherently encoded in EEG signals. Due to varying channel configurations across datasets, they either approximate spatial structure with self-attention or restrict training to a limited set of common channels, sacrificing flexibility and effectiveness. Moreover, although EEG datasets reflect diverse brain states such as emotion, motor, and others, current models rarely learn state-aware representations during self-supervised pre-training. To address these gaps, we propose BrainPro, a large EEG model that introduces a retrieval-based spatial learning block to flexibly capture channel- and region-level interactions across varying electrode layouts, and a brain state-decoupling block that enables state-aware representation learning through parallel encoders with decoupling and region-aware reconstruction losses. This design allows BrainPro to adapt seamlessly to diverse tasks and hardware settings. Pre-trained on an extensive EEG corpus, BrainPro achieves state-of-the-art performance and robust generalization across nine public BCI datasets. Our codes and the pre-trained weights will be released.

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