AybilgeMurat

AybilgeMurat's Stars

• wenbihan/reproducible-image-denoising-state-of-the-art

  Collection of popular and reproducible image denoising works.

  2.4k11710508

• meagmohit/EEG-Datasets

  A list of all public EEG-datasets

  2.3k9415530

• pbashivan/EEGLearn

  A set of functions for supervised feature learning/classification of mental states from EEG based on "EEG images" idea.

  Language:Python7183353221

• swz30/MIRNet

  [ECCV 2020] Learning Enriched Features for Real Image Restoration and Enhancement. SOTA results for image denoising, super-resolution, and image enhancement.

  Language:Python672143896

• swz30/MIRNetv2

  [TPAMI 2022] Learning Enriched Features for Fast Image Restoration and Enhancement. Results on Defocus Deblurring, Denoising, Super-resolution, and image enhancement

  Language:Python42252157

• pranauv1/AI-Video-Translation

  A simple Google Colab notebook which can translate an original video into multiple languages along with lip sync.

  Language:Jupyter Notebook2305543

• tsyoshihara/Alzheimer-s-Classification-EEG

  Alzheimer’s Disease (AD) is the most common neurodegenerative disease. It is typically late onset and can develop substantially before diagnosable symptoms appear. Electroencephalogram (EEG) could potentially serve as a noninvasive diagnostic tool for AD. Machine learning can be helpful in making inferences about changes in frequency bands in EEG data and how these changes relate to neural function. The EEG data was sourced from 2014 paper titled Alzheimer’s disease patients classification through EEG signals processing by Fiscon et al. There were patients with AD, mild cognitive impairment (MCI), and healthy controls. The data was already preprocessed using a fast fourier transform (FFT) to take the data from the time domain to the frequency domain. There were differing levels of effectiveness in terms of classification but generally, Fisher’s discriminant analysis (FDA), relevance vector machine, and random forest approaches were most successful. Due to inconsistent feature importances in different models, conclusions about important frequency bands for classification were not able to be made at this time. Similarly, different frequencies were not able to be localized to different regions of the brain. Further research is necessary to develop more interpretable models for classification.

  Language:Python301411

• OpenNeuroDatasets/ds004504

  OpenNeuro dataset - A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects

  272014

• vineeths96/SVM-and-Neural-Networks

  In this repository, we will explore and compare different methods of learning non-linear classifiers such as SVMs and Neural Networks.

  Language:Python7102

• Sirabhop/Preclinical-AD-EEG-classification

  Classification of Preclinical-Alzheimer's risk group from EEG data and psychological testing data using machine learning.

  Language:Jupyter Notebook6104

• capogluuu/Denoising-Autoencoders-with-Pytorch

  Remove noise from printed text with CNN Autoencoder in Pytorch

  Language:Jupyter Notebook5111

• xmootoo/gsp-alzheimer-detection

  Applying the Graph Discrete Fourier Transform to EEG data for Alzheimer Disease detection.

  Language:Jupyter Notebook5122

• fjssharpsword/MedIR

  Medical Image Retrieval

  Language:Python4101

• HimanshuKhanchandani/Dementia-Detection-Tool

  A machine learning tool using EEG to detect Alzheimers.

  Language:Jupyter Notebook4201

• Yanghuoshan/EEG-based-Alzheimer-s-Disease-Recognition

  Language:Jupyter Notebook4100

• pablomc88/EEG-and-MEG-Proxy-Marker-of-Excitation-Inhibition-Imbalance-in-Alzheimer-s-Disease

  Language:Jupyter Notebook3100

• vishnu-vc/EEG_Disease_Classification

  Ensemble Machine Learning techniques for the classification of subjects with Alzheimer's ,Frontotemporal dementia and Healthy Controls

  Language:Jupyter Notebook310

• duongminhhieu16/Alzheimers-Classification-based-on-EEG

  Language:Python210

• jiapulidoar/EEG-alzheimer

  Repo with the EEG alzheimer dataset

  Language:Jupyter Notebook2100

• joshdunk98/EEG-Network-Analysis

  A new approach to EEG Network Analysis. The code contained in this repository takes sample data of Alzheimer's Disease patients from UK's College of Medicine and creates a causality network to analyze the casual relationship between EEG channels.

  Language:Jupyter Notebook2

• sathiya06/Early-Detection-Of-Alzheimer-s-Disease

  Machine Learning Project For Detection Of Alzheimer's Disease from EEG

  Language:Jupyter Notebook210

• UtkarshRedd/EEG-Parkinsons-Alzheimers-Machine-Learning

  Language:Jupyter Notebook2100

• Aldhubri/Quantile-graphs-for-EEG-based-diagnosis-of-Alzheimer-s-disease-article

  this project related to the article https://www.semanticscholar.org/paper/Quantile-graphs-for-EEG-based-diagnosis-of-disease-Pineda-Ramos/95d5e66e95d23e4b33de8013405c4f28419fa4a0

  Language:Jupyter Notebook1

• FernandoGaGu/NeuGPS

  Diagnostic algorithms for neurodegenerative diseases developed at "Diagnosis of Alzheimer’s disease and behavioral variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms"

  Language:Python1200

• KrishnaRoman/Deep-Gaussian-Processes

  Implementation code of Deep Gaussian Processes and Infinite Neural Network for the Analysis of EEG Signals in Alzheimer's Diseases

  Language:HTML1110

• raafidmv/Nueromatch-Alzheimer-Dimentia-using-EEG

  Language:Jupyter Notebook110

• rene-calz/eeg-fourier

  Analysis with the Fast Fourier Transform of EEGs. We compare the results of a group of Alzheimer patients and healthy people.

  Language:Jupyter Notebook1

• ryanhammonds/alzheimers_eeg

  1100

• siddshashi/ImageDenoising

  Image Denoising of Smartphone Image Denoising Dataset (SIDD) using RIDNet

  Language:Jupyter Notebook110

• sobieddch90/mcd_udg_tfm-egg_analysis

  EEG Data Analysis - TFM UdG

  Language:Jupyter Notebook1100