This toolbox offers 40 feature extraction methods (EMAV, EWL, MAV, WL, SSC, ZC, and etc.) for Electromyography (EMG) signals applications.

A feature extraction toolbox for EMG signals written in MATLAB

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

SDK of sEMG function of matlab

Essential Motor Cortex Signal Processing MATLAB Toolbox which implements various methods for three major aspects of investigating human motor cortex from Neuroscience view point: (1) ERP estimation and quantification, (2) Cortical Functional Connectivity analysis and (3) EMG quantification

A toolbox for offline muscle activity onset/offset detection in multi-channel EMG data

An improved multiscale sample entropy algorithm to extract signal features: "Analysis of fatigue in the biceps brachii by using rapid refined composite multiscale sample entropy"

Algorithms and evaluation toolkit for removing strong cardiac interference from surface EMG measurements

visualEEG is a MATLAB based single window interactive tool for basic EEG/EMG analysis.

Code to analyze high-density EEG and concurrent EMG/EOG datastreams during balance perturbations (replicates results from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6088363/)

The goal is to create a robotic assistive system designed to aid individuals in standing up from a soft chair.

MATLAB implementation of FARM toolbox, for removing MRI artifacts from EMG recordings

Scripts for post-processing HD-sEMG decomposition results. Useful for analyzing Motor Unit (MU) behavior (e.g., firing rate, coherence) and facilitating visual inspection through informative plots.

Biomedical Signal & Image Processing Lab Projects.

Learning the basis of EMG pre-processing

Selecting the best time-domain EMG features using the novel and some classical feature selection algorithms.

Filters developed in Matlab using either the code generated by Matlab's Filter Designer App or difference equations to filter EMG Data

Using 2D and 3D AUC diagrams we provide improved detection accuracy of Parkinson's disease

The objective of our project is to use electromyography (EMG) in order to classify different hand gestures. EMG is a technique for evaluating and recording the electrical activity produced skeletal muscles. We used double channel surface electrodes to record EMG, with 3 electrodes per channel, one for ground and 2 for the differential input. We …