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metadata
title: EEG Mental Arithmetic Task Visualizer
emoji: πŸ’»
colorFrom: green
colorTo: purple
sdk: docker
app_port: 8501
tags:
  - streamlit
pinned: false
short_description: EEG Visualizer & Data Exploration Space

🧠 EEG Mental Arithmetic Explorer

Interactive visualization tool for exploring the EEG During Mental Arithmetic Tasks dataset.

Repository Structure

space/
β”œβ”€β”€ scr/ 
β”œβ”€β”€β”€β”€β”€β”€ app.py           # Main Streamlit application
β”œβ”€β”€ requirements.txt     # Python dependencies
β”œβ”€β”€ README.md            # This file
└── edf_files.zip        # Compressed EDF files (auto-extracted on launch)

The EDF files are automatically extracted when the Space starts.

Features

πŸ“ˆ Signal Viewer

  • Visualize EEG signals from 23 channels
  • Stacked or overlay plot options
  • Adjustable time windows
  • Multi-channel selection

🌈 Spectral Analysis

  • Power Spectral Density (PSD) visualization
  • Frequency band analysis (Delta, Theta, Alpha, Beta, Gamma)
  • Band power comparison
  • Individual channel analysis

πŸ“Š Statistics

  • Channel-wise statistical metrics
  • Correlation matrix heatmap
  • Comprehensive data overview

πŸ‘€ Subject Selection

  • Browse all subjects
  • View performance groups (Good/Poor)
  • Toggle between resting and task states
  • Support for both CSV and EDF formats

Dataset

This explorer uses the EEG During Mental Arithmetic Tasks dataset containing:

  • 36 subjects with paired recordings
  • 23 EEG channels (International 10/20 system)
  • Resting state and mental arithmetic task recordings
  • Performance-based classification

Usage

  1. Select Subject: Choose from 36 available subjects
  2. Choose Recording: Resting state or mental arithmetic task
  3. Select Format: CSV (processed) or EDF (original)
  4. Explore: Navigate through the different visualization tabs

Citation

@article{zyma2019eegmat,
  author = {Zyma, Igor and Tukaev, Sergii and Seleznov, Ivan and others},
  title = {Electroencephalograms during Mental Arithmetic Task Performance},
  journal = {Data},
  volume = {4},
  number = {1},
  year = {2019},
  doi = {10.3390/data4010014}
}

Links