# eeg_depression

**Repository Path**: halfskywalker/eeg_depression

## Basic Information

- **Project Name**: eeg_depression
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-18
- **Last Updated**: 2024-07-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# eeg_depression

The repository contains machine learning models for classification of Major Depression Disorder patients from healthy controls.


The repository provides two approaches: a standard feature-extracted approach and a deep learning approach.
Feature extraction and preparing data were made with https://github.com/ledovsky/eeg-research 

Deep learning approach includes 3 notebooks with such models:
  - 3D Autoencoder on spectrum EEG data
  - 2D Autoencoder on spectrum EEG data
  - 2D CNN model

Standard feature-extracted approach  includes notebook with training different ml models ( Random Forest, Logistic Regression, KNN, Gradient Boosting etc.) , notebook with extracting important features recieved on the best model and notebook with attemt to use transfer learning from one eeg dataset to another