Pinned Repositories
A-Bio-marker-using-Topological-Machine-Learning-of-rs-fMRI
abide-fmri
Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.
ABIDE_dataset_download
Download ABIDE I and ABIDE II dataset
ABIDEI
autism mri dataset in .nii format
Adaptive-Thresholding
Combination of Gamma Gaussian Mixture model and topological FDR for thresholding fMRI statistical maps.
ADHD-Detection-from-EEG-Signal
Signal Processing project: Extraction of Feature from EEG Signal to Detect ADHD
ADHD200-dALFF
Here you can find the files with ALFF and phenotypic values of the ADHD200 database, as well as the results of the classifiers used
All-In-One-EEG-Feature-Extraction-Toolbox
An all-in-one EEG feature extraction toobox, including statistical features, Hjorth parameters, entropy, nonlinear features, power spectral density (PSD), differential entropy (DE), empirical mode decomposition (EMD), common spatial patterns (CSP), microstate analysis and so on. (The list of features will continue to update...)
gsp-alzheimer-detection
Applying the Graph Discrete Fourier Transform to EEG data for Alzheimer Disease detection.
machine_learning_addiction
We use machine learning techniques for identification of the best cognitive markers for cocaine dependence.
MHMOHassan's Repositories
MHMOHassan/All-In-One-EEG-Feature-Extraction-Toolbox
An all-in-one EEG feature extraction toobox, including statistical features, Hjorth parameters, entropy, nonlinear features, power spectral density (PSD), differential entropy (DE), empirical mode decomposition (EMD), common spatial patterns (CSP), microstate analysis and so on. (The list of features will continue to update...)
MHMOHassan/gsp-alzheimer-detection
Applying the Graph Discrete Fourier Transform to EEG data for Alzheimer Disease detection.
MHMOHassan/abide-fmri
Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.
MHMOHassan/ADHD-Detection-from-EEG-Signal
Signal Processing project: Extraction of Feature from EEG Signal to Detect ADHD
MHMOHassan/Analysis-of-fMRI-BOLD-signals-connectivity-estimation-task-subject-identification-network-analysis
These Python notebooks reproduce some figures in the following preprint using the libraries pyMOU and NetDynFlow: https://www.biorxiv.org/content/10.1101/531830v2
MHMOHassan/BCI_competition_III_IVa_analysis
CSP-based EEG feature extraction and visualization and classification tasks
MHMOHassan/crop-classification
Master's Thesis on Crop Classification with Remote Sensing
MHMOHassan/Deep-Learning-Methods-to-Predict-Disease-in-Brain-Images
Use resting state fMRI dataset of ADHD and controls to predict ADHD and evaluate performance on related population of ADHD
MHMOHassan/ds004504
OpenNeuro dataset - A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects
MHMOHassan/ds004504_Aperdiodic
Code for analyzing ds004504 data in aperiodic analysis project
MHMOHassan/ecg-classification
ECG Arrhythmia classification using CNN
MHMOHassan/EEG-Data-Clustering
EEG data: found the time point with the highest activity for each electrode, clustered the data based on time points, visualized the clustered brain areas on a head map. Combination of data preprocessing, feature extraction, clustering, and data visualization tasks to gain insights into the brain activity patterns.
MHMOHassan/EEG-SIGNAL-ANALYSIS-USING-MACHINE-LEARNING
This project uses machine learning algorithms to analyze EEG signals and identify patterns and abnormalities for improved diagnosis and treatment of neurological disorders. It involves pre-processing EEG data, feature extraction, and applying ML models to classify signals into different categories.
MHMOHassan/EEG_clustering_pipeline
R and MATLAB code for our paper "EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents"
MHMOHassan/eeg_fConn
Python library to compute functional connectivity measures from EEG
MHMOHassan/eegbook
MHMOHassan/Electrophysiology-Tutorial-for-Neuroscience
This is a tutorial for key data analysis steps used by neuroscientists - filtering, spike extraction, PCA, clustering, and spectral analysis. It applies to neuroscientists who deal with spikes, LFPs, EEGs and EMGs
MHMOHassan/Epilespy-EEG-Signals
Classify People as Epilespy and Normal people we cleaning DatasSet Then we Do Feature Extraction Then Modeling
MHMOHassan/Extracting-power-bands-theta-delta-bands-from-the-EEG-signal-using-MNE-and-YASA-Python
Extracting power bands (theta, delta, alpha, beta bands) from the EEG signal using MNE and YASA Python
MHMOHassan/hse-coursera-data-scraping
Resources for "Data Scraping" course.
MHMOHassan/Language_Detection_Probability
Some non-ML models for language detection based on probability distributions
MHMOHassan/mea-analysis-pipeline
Analysis pipeline for MEA EEG analysis. Includes average band power, power spectrum, and
MHMOHassan/Microorganism-Classification_using_GANs_and_Deep_learning
This repository contains the code for the research project "Design of Efficient Classification Model for Paramecium and Hydra Microorganisms"
MHMOHassan/mimic-code
MIMIC Code Repository: Code shared by the research community for the MIMIC family of databases
MHMOHassan/network_TDA_tutorial
This repository is dedicated for the tutorial on network and topological neuroscience.
MHMOHassan/neuroimage
set of tools for management of fMRI processing.
MHMOHassan/notebooks
Jupyter Notebooks
MHMOHassan/PortfolioProjects
MHMOHassan/rnn-eeg-ad
EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network
MHMOHassan/topological-comparisons-of-dimension-reduction-rs-fMRI
code repository for project (paper: Dimensionality reduction and topological consistency: parsing representational differences in resting-state fMRI)