openmedical/registry
← registry

EEG-Person-Identification

imported

software/eeg-person-identification

Biometic Systems project, based on "EEG-based user identification system using 1D-convolutional long short-term memory neural networks"

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

EEG-Person-Identification project image
GitHub preview card for Neetx/EEG-Person-Identification. 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
biometric-authentication · biometric-identification · biometrics · cnn · deeplearning · eeg · lstm · machine-learning · 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.

  • deepsleepnetcnn · eeg · lstm

    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

  • Sleep Apnea Classification using Deep Learning on ECG Signals

  • CNN-LSTM based QRS detector for ECG signals

  • Package implementing modular processes for real-time feature localization on ECG and EDA signals.

  • Programming assignments, labs and quizzes from all courses in the Coursera AI for Medicine Specialization offered by deeplearning.ai

  • disease-predictorcnn · deeplearning

    AI disease detection and prediction for humans, plants, and animals. Complete ML project with custom training, offline operation, no API keys. Detect diseases from images using deep learning and…

sources
  1. api.github.com/repos/Neetx/EEG-Person-Identification
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2021-11-23, 11 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/35.json→ .entries["eeg-person-identification"]

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