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ds4a

Jupyter notebooks cases from Data Science for All 2020.

ec_estimation

(version 2016) noise-diffusion network model to interpret fMRI data: estimation of intracortical interactions with DTI topological constraints

ecg-analysis

ECG analysis to classify anterior myocardial infarction cases.

ecg-classification

ECG Arrhythmia classification using CNN

eeg-and-confusion

Data from a study measuring EEG signals during confused states is visualized and analyzed for feature extraction.

eeg-classification

This project was a joint effort with the neurology labs at UNL and UCD Anschutz to use deep learning to classify EEG data.

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.

eeg-datasets

A list of all public EEG-datasets

eeg-emg-analytics

This repository contains a set of Matlab scripts to process EEG and EMG signals (feature extraction, spectral analysis, ...).

eeg-feature-extraction-toolbox

This toolbox offers 30 types of EEG feature extraction methods (HA, HM, HC, and etc.) for Electroencephalogram (EEG) applications.

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.

eeg_emotion_classifier_deap

EEG Emotion classification using the DEAP pre-processed data

eegbook

end-to-end-time-series

This repository hosts code for my Time Series videos part of playlist here - https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK

epilespy-eeg-signals

Classify People as Epilespy and Normal people we cleaning DatasSet Then we Do Feature Extraction Then Modeling

event_related_fmri_tda

Repository for event-related fMRI simulation for testing Topological Data Analysis

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

feature-extraction-eeg

Feature Extraction of Mental Load EEG signals

frr-vs-dtw

This project is to compare our Fisher-Rao registration framework to the Dynamic Time Warping (DTW) to analyze neural signals such as EEG and fMRI where phase variability plays an important role in the data. The code provided here illustrate comparisons with simulation examples.

gaussian-processes-regression-tutorial

An Intuitive Tutorial to Gaussian Processes Regression

gradientdescentexample

Example demonstrating how gradient descent may be used to solve a linear regression problem

gsp-alzheimer-detection

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

hse-coursera-data-scraping

Resources for "Data Scraping" course.

hst953_iomed

HST.953 Workshop: Using MIMIC/eICU I/O and Medication Data

iciar2018

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. ICIAR 2018 Grand Challenge on BreAst Cancer Histology images (BACH)

imagined-speech

Classifying syllables in Imagined Speech using EEG

imaging_in_r

Neuroimaging short course

language_detection_probability

Some non-ML models for language detection based on probability distributions

machine_learning_addiction

We use machine learning techniques for identification of the best cognitive markers for cocaine dependence.

machine_learning_bpd

Scripts and data to reproduce the results in paper: "Machine learning detects predictors of clinical change after Dialectical Behavior Therapy in Borderline Personality Disorder". Preprint: https://psyarxiv.com/pm5rz/