https://github.com/exios66/multi-modal-analysis

A Python application tailored for the analysis of psychometric, vitals, and neurological imaging data. The application is designed to ingest diverse data types, preprocess them, extract meaningful features, analyze correlations, and visualize the results.
https://github.com/exios66/multi-modal-analysis

Last synced: about 2 months ago
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A Python application tailored for the analysis of psychometric, vitals, and neurological imaging data. The application is designed to ingest diverse data types, preprocess them, extract meaningful features, analyze correlations, and visualize the results.

• Host: GitHub
• URL: https://github.com/exios66/multi-modal-analysis
• Owner: Exios66
• License: mit
• Created: 2024-10-23T08:14:27.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-23T21:29:31.000Z (3 months ago)
• Last Synced: 2024-10-24T09:52:35.374Z (3 months ago)
• Language: Python
• Size: 50.8 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Multi-Modal Analysis Pipeline

A comprehensive Python application for analyzing psychometric, vitals, and neurological imaging data. This application provides a robust pipeline for data ingestion, preprocessing, feature extraction, correlation analysis, and machine learning modeling.

## Overview

This pipeline enables researchers and data scientists to process and analyze multi-modal data from various sources including psychometric assessments, physiological measurements, and neurological imaging. The system automates the entire workflow from raw data ingestion through analysis and visualization.

## Features

- Multi-modal data processing support:

  - Eye tracking data (fixations, saccades, pupil dilation)

  - EEG data (raw signals, frequency bands, ERPs)

  - Survey responses (psychometric scales, questionnaires)

  - Vital signs (heart rate, GSR, breathing rate)

  - Face heat maps (thermal imaging data)

  - Motion capture data

  - Voice recordings

- Automated data preprocessing and cleaning

  - Artifact removal

  - Signal filtering

  - Missing data imputation

  - Outlier detection

- Advanced feature extraction

  - Time-domain features

  - Frequency-domain features  

  - Statistical features

  - Cross-modal features

- Statistical analysis

  - Correlation analysis

  - Factor analysis

  - Time series analysis

  - Dimensionality reduction

- Machine learning capabilities

  - Supervised learning models

  - Unsupervised clustering

  - Deep learning integration

  - Cross-validation

- Visualization tools

  - Interactive plots

  - 3D visualizations

  - Time series plots

  - Statistical charts

- Production features

  - Comprehensive logging

  - Error handling

  - Progress tracking

  - Performance optimization

  - Parallel processing

- Configurable pipeline parameters

- Modular, extensible architecture

## Requirements

- Python 3.8+

- CUDA-capable GPU (optional, for deep learning)

- 16GB+ RAM recommended

- 100GB+ storage space for data

## Installation

1. Clone the repository: