mhmohassan Goto Github PK

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...)

alzheimer-s-classification--university-of-turku-finland

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

asd_gp_gcn

ASD diagnosis on ABIDE I

bci_competition_iii_iva_analysis

CSP-based EEG feature extraction and visualization and classification tasks

benchmark_rsfmri_prediction

Minimal scripts available for predictions on multiple datasets

bootcamp-idao-2022

IDAO 2022: Machine Learning Bootcamp

brain-tumour-mri-classification

Final project for the Monash University Data Bootcamp. An app that can classify MRIs of the brain and predict whether a tumor is present and what kind of tumour. It then stores the results in an SQL database for future reference

classification-of-alcoholic-eeg-signals-using-wavelet-scattering-transform-based-features

The code used to generate results presented in "Classification of alcoholic EEG signals using wavelet scattering transform-based features" , Computers in Biology and Medicine, 2021

cnn-hyperparameters-optimization-using-metaheuristic-optimizers

CNN Hyperparameters Optimization using Metaheuristic Optimizers

cocaine-dependence

Code and data for reproducing key results in the paper "Utility of Machine-Learning Approaches to Identify Behavioral Markers for Substance Use Disorders: Impulsivity Dimensions as Predictors of Current Cocaine Dependence".

compucademy-data-science

computational_physics

congestive-heart-failure-detection

This is the implementation of "Congestive heart failure detection using random forest classifier" paper by Zerina Masetic and Abdulhamit Subasi.

coursera

For educational purposes only!

coursera-machine-learning-and-data-analysis

crop-classification

Master's Thesis on Crop Classification with Remote Sensing

csss22-tda

Topological Data Analysis of fMRI and high-throughput sequencing data

dbmi_eeg_clustering

Clusters EEG Signals

deap_mne_preprocessing

Scripts to a) download DEAP EEG dataset b) preprocess its EEG signals and c) perform feature extraction

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

delay-discounting

Delay-discounting task measuring impulsivity

detection-and-classification-of-alzheimers-disease

The purpose of this paper is to detect Alzheimer’s Disease using Deep Learning and Machine Learning algorithms on the early basis which is being further optimized using CSA(Crow Search Algorithm). Alzheimer’s is one of kind and fatal. The early detection of Alzheimer’s Disease because of it’s progressive risk and patients all around the world. Earl