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DL-EEG

imported

software/dl-eeg

A Deep Learning Library for State-Based EEG Analysis

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

DL-EEG project image
GitHub preview card for konspatl/DL-EEG. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
deep-learning · eeg · electroencephalography · keras · mne · neuroimaging · neuroscience · python · tensorflow
Regulatory
unknown
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.

  • pyprepeeg · mne · neuroimaging · neuroscience

    PyPREP: A Python implementation of the Preprocessing Pipeline (PREP) for EEG data

  • mne-bidseeg · electroencephalography · mne · neuroimaging

    MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python.

  • niseqeeg · electroencephalography · neuroimaging · neuroscience

    group sequential tests for neuroimaging

  • Brain-tumor-classifierkeras · neuroimaging · tensorflow

    Brain tumor classification model from MRI scans using a Convolutional Neural Newtwork (CNN) built with Tensor flow/Keras.

  • arl-eegmodelseeg · keras · tensorflow

    This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

  • EEG_classificationeeg · keras · tensorflow

    EEG Sleep stage classification using CNN with Keras

sources
  1. api.github.com/repos/konspatl/DL-EEG
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2023-05-30, 5 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/11.json→ .entries["dl-eeg"]

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