PiEEG Shop

JNEEG

Low-cost brain-computer interface with shield JNEEG (Shield for Jetson Nano , nvidia) to measure EEG, EMG, and ECG bio-signals for 8 EEG channels.

JNEEG is a special tool for the Jetson Nano that can measure signals from your body like brainwaves, muscle activity, and heartbeats. It’s free for anyone to use and works with different kinds of sensors. You just have to attach the sensors and run a simple program in Python to start collecting data. People can use it for lots of things like playing games, watching movies, exercising, staying healthy, relaxing, and more.

JNEEG is not a medical device and has not been certified by any government regulatory agency for use with the human body. You are fully responsible for your personal decision to purchase this device and, ultimately, for its safe use. Please read LIABILITIES.

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Features & Specifications

  • Compatible with Jetson Nano
  • 8 channels for connecting wet or dry electrodes
  • Data transfer via SPI protocol with a frequency from 250 SPS to 16 kSPS and a resolution of 24 bits per channel
  • Programmable signal gain: 1, 2, 4, 6, 8, 12, 24
  • Ability to measure impedance
  • Easy programming with the provided open-source software for reading and processing data in Python

All technical details on GitHub , YouTube

Why Is JNEEG

JNEEG was created specially for the task of using Machine learning and Deep learning to make signal processing and feature extraction for EEG and other bio signals.

The Jetson Nano is considered one of the most respected single board computers in the world for real-time machine learning applications, which have found wide application in computer vision applications, and especially robot control. ,Using JNEEG and Jetson Nano together opens up new ,opportunities to delve deeper into neuroscience with deep learning. This combination enables seamless real-time EEG data reading and signal processing directly on the Jetson nano, eliminating the need for data transfer and additional computing resources for feature extraction and signal processing.
We provide comprehensive software packages as well as all necessary technical documentation and extensive user support for JNEEG device enthusiasts.

Potential application 

EEG Control of a Robotic Wheelchair

A portable, self-contained neuroprosthetic hand with deep learning-based finger control

Auditory Brainwave Entrainment System using Time-Series Analysis of EEG Signal

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

 

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