openmedical/registry
← registry

CBraMod

imported

software/cbramod

[ICLR 2025] CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
active
Maturity
deployed
Organization
unknown
Country
unknown
Documentation
unknown
Tags
artificial-intelligence · eeg · eeg-decoding · foundation-model · machine-learning · pretrained-models
Regulatory
unknown
built by · 1

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • EEGMambaeeg · pretrained-models

    [Neural Networks 2025] EEGMamba: An EEG Foundation Model with Mamba

  • SynthSleepNeteeg · foundation-model

    [IEEE TCYB] Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework

  • breast_cancer_classifierpretrained-models

    Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

  • CLEARfoundation-model

    CLEAR: an auditable foundation model for radiology grounded in clinical concepts

  • Large-Scale-Medicalpretrained-models

    [TPAMI 2026] Large-Scale 3D Medical Image Pre-training with Geometric Context Priors

  • MEMEfoundation-model

    [npj Digital Medicine 2025] Multiple Embedding Model for EHR (MEME) used for strong prediction on Emergency Department tasks

sources
  1. api.github.com/repos/wjq-learning/CBraMod
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2026-06-16, 331 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/35.json→ .entries["cbramod"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.