monsteryi0314

monsteryi0314's Stars

• AshleyLab/deepbeat

  DeepBeat: Multi-task deep learning for cardiac rhythm detection in wearable devices

  Language:Jupyter Notebook228

• siddhanthsatish/Healthify

  Detected the number of steps taken from iPhone's accelerometer data using a step-detection algorithm with 99% accuracy. Developed an activity recognition classifier to identify whether the user is cycling, walking, jumping, or sitting with 85% accuracy. Derived heart rate and breath rate from Photoplethysmography (PPG) signal using signal filtering techniques. Technologies Used: Python Numpy, SciPy, Matplotlib, Jupyter Notebooks.

  Language:Jupyter Notebook82

• thomasthaddeus/DataAnalysisToolkit

  DataAnalysisToolkit is a Python-based data analysis tool designed to streamline various data analysis tasks. It provides the ability to load data from CSV files, perform statistical calculations, detect outliers, clean data, and visualize data.

  Language:Jupyter Notebook4

• hpi-dhc/pypg

  PyPG: A library for PPG (PhotoPlethysmoGram) processing

  Language:Python63

• talhaanwarch/PPG_mental_stress

  Diagnosis of Mental Stress using Evolutionary Algorithm and Machine Learning

  Language:Jupyter Notebook54

• smalvar/PPG-RemoveMotion

  Remove motion using PPG and ECG signals (wearable)

  Language:HTML73

• hiredd/PPG-Pattern-Recognition

  Pattern recognition in PPG signals using only a limited number of labeled examples. We train a network to separate clean segments from noise and motion artifacts.

  Language:Python313

• pmwaniki/ppg-analysis

  Using self-supervised learning to extract features from PPG signals

  Language:Python155

• hiredd/PPG-Heart-Rate-Classifier

  Extract quality heart rate segments from noisy/motion affected raw PPG measurements

  Language:Python518

• KJStrand/Pulse_Rate_Estimation

  Motion Compensated Pulse Rate Estimation from PPG and Accelerometer Sensor Data

  Language:Jupyter Notebook157

• dedeus10/BloodPressure_PPG_ML

  Repository from final conclusion work of Computer Engineering. Estimation of Systolic and Diastolic Blood Pressure Using PPG and ECG Signals and Machine Learning Algorithms

  Language:Python4519

• jeya-maria-jose/Cuff_less_BP_Prediction

  Prediction of Blood Pressure from ECG and PPG signals using regression methods.

  Language:Python14646

• thuml/Time-Series-Library

  A Library for Advanced Deep Time Series Models.

  Language:Python6.2k987

• qeebeast7/SFCSAN

  Language:Python112

• ThreePoundUniverse/MACTN

  Language:Python62

• linkingct/gnn-deap-eeg

  http://www.eecs.qmul.ac.uk/mmv/datasets/deap/

  277

• tuengominh/deap-eeg-classification

  EEG-based emotion classification using DEAP dataset

  Language:Jupyter Notebook11527

• yi-ding-cs/TSception

  [TAFFC-2022] PyTorch implementation of TSception v2

  Language:Python8420

• isfengg/Graphormer

  8

• xi178568666/graphormer

  Language:Jupyter Notebook72

• AniketRajpoot/Emotion-Recognition-Transformers

  SOTA methods for performing emotion classification using Transformers.

  Language:Python193

• OpenSci-CN/chinese-open-science-network.github.io

  Language:JavaScript42

• YichenTang97/BiHDM_pytorch

  An unofficial pytorch implementation of the BiHDM model proposed by Yang et al. for decoding emotion from multi-channel EEG recordings, with scikit-learn compatibility.

  Language:Python111

• xueyunlong12589/DGCNN

  Repetition code of the model for the paper "EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks" in pytorch

  Language:Python8517

• nadzeri/Realtime-EEG-Based-Emotion-Recognition

  Realtime emotion recognition from EEG data.

  Language:Python13957

• chongwar/gnn-eeg

  Implementation of graph convolutional networks based on PyTorch Geometric to classify EEG signals.

  Language:Python485

• eeyhsong/EEG-Conformer

  EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.

  Language:Python38855

• monsteryi0314/eeg-gcnn

  Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.

  1

• neerajwagh/eeg-gcnn

  Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.

  Language:Python15030

• kaixindelele/ChatPaper

  Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复

  Language:Python18.2k1.9k