https://github.com/eomielan/eeg-machine-learning

Comparison of Machine Learning models for identifying whether a person is engaged in active recall or problem solving using EEG data
https://github.com/eomielan/eeg-machine-learning

Last synced: about 9 hours ago
JSON representation

Comparison of Machine Learning models for identifying whether a person is engaged in active recall or problem solving using EEG data

• Host: GitHub
• URL: https://github.com/eomielan/eeg-machine-learning
• Owner: eomielan
• Created: 2023-12-03T21:15:35.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-08-15T06:12:42.000Z (3 months ago)
• Last Synced: 2024-08-15T07:43:25.278Z (3 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 32.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Active Recall or Problem Solving

Our project's goal was to develop a machine learning model to predict whether a user is engaged in problem-solving or memory recall tasks based on EEG data from the Muse 2 device. By analyzing brainwave patterns and additional metrics like heart rate and movement, we used the MindMonitor app to extract and process data beyond the device's standard functions. Participants completed a maze for problem-solving and a rapid recall game for memory tasks. Our objective was to create a reliable classification system to understand brain activity during cognitive tasks, providing insights into how the brain functions during intensive activities like exams.