openmedical/registry
← registry

nnspt

imported

software/nnspt

A Python library for signal processing with PyTorch. Useful for machine learning.

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

nnspt project image
GitHub preview card for rostepifanov/nnspt. 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 · ecg · eeg · emg · machine-learning · python · signal-processing
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.

  • brainflowecg · eeg · emg · signal-processing

    BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors

  • sigcleanecg · eeg · emg · signal-processing

    SigClean is a comprehensive Python library for cleaning and preprocessing biomedical signals including ECG, EMG, EEG, and other physiological signals. It provides a complete toolkit for signal…

  • EEG-EMG-analyticseeg · emg · signal-processing

    This repository contains a set of Matlab scripts to process EEG and EMG signals (feature extraction, spectral analysis, ...).

  • BrainFlowAndroidTestecg · eeg · emg

    App for BrainFlow developers to test it on Android

  • eegsynthecg · eeg · emg

    Converting real-time EEG into sounds, music and visual effects

  • Low-Cost-EEG-Based-BCIecg · eeg · emg

    Low Cost Electroencephalogram Based Brain-Computer-Interface

sources
  1. api.github.com/repos/rostepifanov/nnspt
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2024-10-24, 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/30.json→ .entries["nnspt"]

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