RRepoGEO

REPOGEO REPORT · LITE

935963004/LaBraM

Default branch main · commit c431221e · scanned 6/9/2026, 3:23:06 PM

GitHub: 624 stars · 113 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface 935963004/LaBraM, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific topics for EEG, BCI, and foundation models

    Why:

    COPY-PASTE FIX
    eeg, bci, brain-computer-interface, foundation-model, large-brain-model, deep-learning, representation-learning, iclr-2024
  • highreadme#2
    Reposition the README's opening to clearly state the project's domain and purpose

    Why:

    CURRENT
    # LaBraM

Official implementation of our ICLR 2024 paper:
**Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI**

## Abstract
    COPY-PASTE FIX
    # LaBraM: A Large Brain Model for EEG and BCI

LaBraM is a foundation model specifically designed for learning generic representations from electroencephalogram (EEG) data across various brain-computer interface (BCI) applications. This repository provides the official implementation of our ICLR 2024 paper:
**Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI**

## Abstract
  • mediumhomepage#3
    Update homepage link to the paper's abstract page instead of direct PDF

    Why:

    CURRENT
    https://openreview.net/pdf?id=QzTpTRVtr
    COPY-PASTE FIX
    https://openreview.net/forum?id=QzTpTRVtr

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface 935963004/LaBraM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Braindecode
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Braindecode · recommended 1×
  2. MNE-Python · recommended 1×
  3. PyTorch · recommended 1×
  4. TensorFlow · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How to pre-train a universal model for various EEG signal processing tasks?
    you: not recommended
    AI recommended (in order):
    1. Braindecode
    2. MNE-Python
    3. PyTorch
    4. TensorFlow
    5. Hugging Face Transformers
    6. SimCLR
    7. MoCo
    8. BYOL
    9. VAEs
    10. Denoising Autoencoders
    11. Perceiver IO
    12. Gato

    AI recommended 12 alternatives but never named 935963004/LaBraM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a foundation model to handle diverse EEG datasets for brain-computer interface applications.
    you: not recommended
    AI recommended (in order):
    1. Braindecode (braindecode/braindecode)
    2. MNE-Python (mne-tools/mne-python)
    3. TensorFlow (tensorflow/tensorflow)
    4. PyTorch (pytorch/pytorch)
    5. EEG-DL (eeg-dl/eeg-dl)
    6. Hugging Face Transformers (huggingface/transformers)
    7. OpenBCI GUI (OpenBCI/OpenBCI_GUI)
    8. BCI2000 (bci2000/bci2000)

    AI recommended 8 alternatives but never named 935963004/LaBraM. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of 935963004/LaBraM?
    pass
    AI named 935963004/LaBraM explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts 935963004/LaBraM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 935963004/LaBraM explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo 935963004/LaBraM solve, and who is the primary audience?
    pass
    AI named 935963004/LaBraM explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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935963004/LaBraM — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite