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yaqi.chu's Projects

aimoneyhunter icon aimoneyhunter

ai副业赚钱大集合,教你如何利用ai做一些副业项目,赚取更多额外收益。

aizmeng.github.io icon aizmeng.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

awesome-llm-robotics icon awesome-llm-robotics

A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites

bbci_public icon bbci_public

MATLAB toolbox for Brain-Computer Interfacing (BCI)

bcilab icon bcilab

MATLAB Toolbox for Brain-Computer Interface Research

braindecode icon braindecode

A deep learning toolbox to decode raw time-domain EEG.

braingamesimulator icon braingamesimulator

Brain Game Simulator | NeuroSky Mindwave | Control Windows Games Using Brain Signals

chainqueen icon chainqueen

A Real-Time Differentiable Physical Simulator for Soft Robotics (ICRA 2019 Submission)

chatgpt-next-web icon chatgpt-next-web

One-Click to deploy well-designed ChatGPT web UI on Vercel. 一键拥有你自己的 ChatGPT 网页服务。

clinica icon clinica

Software platform for clinical neuroimaging studies

cranium icon cranium

🤖 A portable, header-only, artificial neural network library written in C99

deeplearning4j icon deeplearning4j

Deeplearning4j, ND4J, DataVec and more - deep learning & linear algebra for Java/Scala with GPUs + Spark

deeplearntoolbox icon deeplearntoolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

ecg-kit icon ecg-kit

A Matlab toolbox for cardiovascular signal processing

edbn icon edbn

Event-based deep belief network offline training algorithm

eeglab icon eeglab

EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD

emg-movement-recognition icon emg-movement-recognition

The source code for the the manuscript titled [M. AbdelMaseeh, T. W. Chen and D. W. Stashuk, "Extraction and Classification of Multichannel Electromyographic Activation Trajectories for Hand Movement Recognition," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 6, pp. 662-673, June 2016.]. The paper proposes a system for hand movement recognition using multi-channel electromyographic (EMG) signals obtained from the forearm surface. This system can be potentially used to control prostheses or to provide input to a wide range of human computer interface systems. The developed methods were tested with the publicly available NINAPro database.

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