Upupleee

emg-data-analysis

Surface EMG signal - Feature Extraction

network_data_streaming_samples

crazyflie-suite

Flight and data analysis framework for Crazyflies.

paparazzi

Paparazzi is a free and open-source hardware and software project for unmanned (air) vehicles. This is the main software repository.

Online-hybrid-BCI

This repository contains an implementations of different hybrid BCI methods for simultaneous EEG and EMG decoding

MyoToolkit

A list of all third party code written for the Myo armband

sEMG-Cross-correlation

This programs corrects the discrepancy between the data acquired by two Myo devices by using the cross-correlation function.

ICA-sEMG

Uses the ICA algorithm to separate a set of sEMG source signals from a set of mixed sEMG signals

semg-bss

Decomposition of sEMG signals via Blind Source Separation

EMG_Database

Python code for building an sEMG Database

Neurotron

Hand pose estimation from sEMG using automated depth labelling and predicting with deep LSTMs

Single-Channel-sEMG-Decomposition

Code for our paper

surface-EMG

sEMG function in MATLAB

YouTube-Blog

Codes to complement YouTube videos and blog posts on Medium.

IMU-Vive-Kinematics

Extracting Kinematics Using Wearable Sensors Code

IMU_Kinematics

Joint angle prediction from Inertial Measurement Unit data.

GP-RNN_UAI2019

Implementaion of Gaussian Process Recurrent Neural Networks developed in "Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks", Qi She, Anqi Wu, UAI2019

NeuroGloves

Using the Myo to play VR games using SteamVR

thesis_code

7semester-project

10th-semsester-code-

Repository for the m.code

PyCYB

Python implementation of joint angles estimates and regression from EMG

TCN_sequential_regression

Temporal Convolutional NNetwork to predict behavioral data (EMG / kinematics) from neural recordings

Robotic-EXoskeleton-for-Arm-Rehabilitation-REXAR-

Rehabilitation of people afflicted with elbow joint ailments is quite challenging. Studies reveal that rehabilitation through robotic devices exhibits promising results, in particular exoskeleton robots. In this work, 1 degree of freedom active upper-limb exoskeleton robot with artificial intelligence aided myoelectric control system has been developed for elbow joint rehabilitation. The raw surface electromyogram (sEMG) signals from seventeen different subjects for five different elbow joint angles were acquired using the Myo armband. Time-domain statistical features such as waveform length, root mean square, variance, and a number of zero crossings were extracted and the most advantageous feature was investigated for Artificial Neural Network (ANN) – a backpropagation neural network with Levenberg-Marquardt training algorithm and Support Vector Machine (SVM) – with Gaussian kernel. The results show that waveform length consumes the least amount of computation time. With waveform length as an input feature, ANN and SVM exhibited an average overall classification accuracy of 91.33% and 91.03% respectively. Moreover, SVM consumed 36% more time than ANN or classification.

gesture-sEMG

The dataset of sEMG from biceps and triceps, in different bending angle of elbow.

Master-Thesis

This is my Master Thesis - Overleaf setup

sEMG

bpnn

A very simple python back propagation neural network 一个非常简单、原始的bpnn的实现，但好懂

BPNN-regression-and-classify

1、BP-momentum神经网络numpy实现及Pytorch实现及各optim在AQI数据集的表现。2、BP网络分类

classification_BPNeuralNetwork

Python 基于BP神经网络实现不同直径圆的分类