Our paper titled A Unified fNIRS Classification Framework Informed by Local Brain Activation Patterns has been accepted by IEEE Transactions on Industrial Informatics (https://ieeexplore.ieee.org/document/9670659).

Our code, including dataset processing, models, training, and evaluation, will be released to this repository.

Recent studies have focused on task-specific and neuroscience-agnostic fNIRS classification models rather than a unified neuroscience-informed framework. We propose LoBrAFrame, a unified, neuroscience-informed fNIRS classification framework that leverages local brain activation patterns through a shared weight mechanism. Within this framework, researchers can easily enhance classification performance using simple or off-the-shelf methods, without redesigning complex models. To instantiate a concrete model, we introduce Mamba, a state space model, into the fNIRS domain and propose LoBrAMamba. Our work will inspire interest in neuroscience-informed fNIRS frameworks.

If you found the study useful for you, please consider citing it.

@ARTICLE{Wang2025LoBrAFrame,
  author={Wang, Zenghui and Du, Songlin},
  journal={IEEE Transactions on Industrial Informatics}, 
  title={A Unified fNIRS Classification Framework Informed by Local Brain Activation Patterns}, 
  year={2025},
  volume={},
  number={},
  pages={1-11},
  doi={10.1109/TII.2025.3632147}}