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EEG-Conformer

imported

software/eeg-conformer

[TNSRE 23] EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.

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

EEG-Conformer project image
GitHub preview card for eeyhsong/EEG-Conformer. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
GPL-3.0(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
activation-map · attention · conformer · eeg · eeg-transformer · eeg-visualization · transformer
Regulatory
unknown
built by · 2

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.

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    Presented @ AIX-Summit | Neuroscope is a EEG research data visualization platform to help EEG researchers. 11 different file formats, TONS of adjustable parameters, and all processing is local for…

  • UnfoldMakie.jleeg · eeg-visualization

    Plotting and visualization tools for EEG data, with additional Unfold.jl regression ERP methods. Based on the visualization libraries Makie.jl and AlgebraOfGraphics.jl

  • Medical-Transformerattention · transformer

    Official Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" - MICCAI 2021

  • A 3D conformational based molecular descriptor set for use in QSPR and Machine Learning.

sources
  1. api.github.com/repos/eeyhsong/EEG-Conformer
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2024-07-24, 758 stars, license reported as GPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/20.json→ .entries["eeg-conformer"]

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