# Brant-X: A Unified Physiological Signal Alignment Framework

arXiv: https://arxiv.org/abs/2409.00122 · HTML: https://mlsanity.com/paper/2409.00122/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 77 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks](https://mlsanity.com/paper/2305.11175/) (2023)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://mlsanity.com/paper/2206.08496/) (2022)
- [Unified Contrastive Learning in Image-Text-Label Space](https://mlsanity.com/paper/2204.03610/) (2022)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning](https://mlsanity.com/paper/2210.06044/) (2022)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [Inter-database validation of a deep learning approach for automatic sleep scoring](https://mlsanity.com/paper/2009.10365/) (2020)
- [MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals](https://mlsanity.com/paper/2306.13102/) (2023)
- [BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning](https://mlsanity.com/paper/2306.13101/) (2023)
- [L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging](https://mlsanity.com/paper/2301.03441/) (2023)
- [Generalizable Sleep Staging via Multi-Level Domain Alignment](https://mlsanity.com/paper/2401.05363/) (2023)
- [TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2203.16336/) (2022)

## Built on by (most cited first, 17 of 17)

- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model](https://mlsanity.com/paper/2506.09110/) (2025)
- [Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities](https://mlsanity.com/paper/2504.19596/) (2025)
- [LEAD: An EEG Foundation Model for Alzheimer's Disease Detection](https://mlsanity.com/paper/2502.01678/) (2025)
- [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026)
- [THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations](https://mlsanity.com/paper/2511.13733/) (2025)
- [Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework](https://mlsanity.com/paper/2502.17481/) (2025)
- [A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications](https://mlsanity.com/paper/2410.08224/) (2024)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [ALFEE: Adaptive Large Foundation Model for EEG Representation](https://mlsanity.com/paper/2505.06291/) (2025)
- [ZUNA: Flexible EEG Superresolution with Position-Aware Diffusion Autoencoders](https://mlsanity.com/paper/2602.18478/) (2026)
- [Bridging Brain with Foundation Models through Self-Supervised Learning](https://mlsanity.com/paper/2506.16009/) (2025)
- [BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals](https://mlsanity.com/paper/2510.02276/) (2025)
- [Next-Token Prediction Learns Generalisable Representations of Sleep Physiology](https://mlsanity.com/paper/2606.09605/) (2026)
- [MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning](https://mlsanity.com/paper/2607.21402/) (2026)
- [EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks](https://mlsanity.com/paper/2606.01767/) (2026)
- [ZeroMAG: Zero-Shot Multimodal Adapter Generation for Plug-and-Play EEG Foundation Models](https://mlsanity.com/paper/2610.03546/) (2026)

## Abstract (arXiv)

Physiological signals serve as indispensable clues for understanding various physiological states of human bodies. Most existing works have focused on a single type of physiological signals for a range of application scenarios. However, as the body is a holistic biological system, the inherent interconnection among various physiological data should not be neglected. In particular, given the brain's role as the control center for vital activities, electroencephalogram (EEG) exhibits significant correlations with other physiological signals. Therefore, the correlation between EEG and other physiological signals holds potential to improve performance in various scenarios. Nevertheless, achieving this goal is still constrained by several challenges: the scarcity of simultaneously collected physiological data, the differences in correlations between various signals, and the correlation differences between various tasks. To address these issues, we propose a unified physiological signal alignment framework, Brant-X, to model the correlation between EEG and other signals. Our approach (1) employs the EEG foundation model to data-efficiently transfer the rich knowledge in EEG to other physiological signals, and (2) introduces the two-level alignment to fully align the semantics of EEG and other signals from different semantic scales. In the experiments, Brant-X achieves state-of-the-art performance compared with task-agnostic and task-specific baselines on various downstream tasks in diverse scenarios, including sleep stage classification, emotion recognition, freezing of gaits detection, and eye movement communication. Moreover, the analysis on the arrhythmia detection task and the visualization in case study further illustrate the effectiveness of Brant-X in the knowledge transfer from EEG to other physiological signals. The model's homepage is at https://github.com/zjunet/Brant-X/.