emg-signal

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

BioAmp EXG Pill is a small and elegant Analog Front End (AFE) board for BioPotential signal acquisition.

A Python package for physyological's signals processing

A custom sEMG dataset developed with the Myo Armband for hand gesture classification

Preprocessing and classify EMG signals, using Tensorflow and Tensorflow Lite to deploy an AI model in a ESP32C3

Surface EMG signal - Feature Extraction

🍯 A RxJava library to access Raw EMG data from your Myo 📈 (plus an Android companion App 📱)

Electromyography (EMG) based convolutional neural network for hand gesture classification.

AI-powered gesture classification from EMG signals

EMG sensor signal sampling and DSP filtering on an embedded STM32 board with he use of miosix RTOS

Controlling your Overwatch character with electromyography sensors (EMG).

Remote robot controlling using EMG sensor

BMEN 3311 (Biomedical Signal Analysis): Reads in biomedical data from a patient’s files into a script and plots the data. Then take the discrete Fourier transforms of each signal then plot the frequency Spectrums. In addition, create a digital notch filter to remove the noise coming from mains power in the ECG signal without creating a phase shi…

Basic code to use EMG-826 board

EMG-Controlled Cooperative Game made in Unity 2D with Cometa Wireless EMG Devices.

A Final Project Program, Design and Build of Medical Rehabilitation Device for People with Paralysis on Hand Based on EMG Sensor

A complete MataLab laboratory for training and evaluating EMG HGR RL DQN and DDQN models.

An advanced AI-powered robotic arm that uses EMG sensors and intelligent control to respond to muscle signals and recognize users for personalized, real-time interaction.

Classification of EMG Signal Using Convolution Neural Network (Sliding Window Technique, Root Mean Square , Fourier Transform)

Full-stack 6-ch dry-electrode sEMG wearable (ESP32/BLE, 1 kHz/ch, JSON presets) with DC removal + HPF/LPF + Kalman DSP and dual-path gesture control (Amplitude thresholds + 6-ch Radar-shape matching) + Flutter app; SNR>=8 dB, latency<=80-100 ms, >=85% online (4 grips)