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BrainLink: Mind-Powered Question Response System

Overview

Brain Link is an innovative project that leverages EEG technology to enable users to answer yes/no questions using brain wave activity. Designed to assist individuals with limited mobility or communication abilities, this system uses non-invasive brain wave analysis to interpret responses through directional thought patterns.

The project is highly customizable, allowing caregivers or administrators to configure personalized question sets in a binary tree structure for dynamic question navigation. It also supports multilingual functionality, providing accessibility for users from diverse linguistic backgrounds.


Features

  • Non-invasive EEG-based communication: Interpret brain wave activity for answering yes/no questions.
  • Dynamic questionnaire: Questions are stored in a binary tree format and adapt based on user responses.
  • Multilingual support: Questions can be presented in various languages as per user preferences.
  • Customizable: Easily update the questionnaire to cater to individual needs.
  • Scalable architecture: Future enhancements can include more complex question-answer patterns and better signal analysis.

How It Works

  1. Hardware Integration: Uses the Neuphony FlexCap EEG headset to capture brain activity.
  2. Signal Preprocessing: Data preprocessing using Python libraries like pylsl for accurate signal analysis.
  3. Custom Model: Combines CNN and RNN layers built in PyTorch to classify brain activity into three categories.
  4. Dynamic Question Flow: Employs binary tree traversal to dynamically present questions based on responses.
  5. GUI Interaction: Intuitive interface built using Python’s Tkinter library for user interaction and configuration.

Folder Structure

  • gui.py: main code which starts the application.
  • lslpipe.py: code to fetch data from lslstream.
  • modelTrain3.py: code for Model.
  • qutionConfig.py: code to start application which fills questionnaire.

About

Developed an assistive AI tool using EEG signals to enable yes/no responses for users with limited mobility. Built a CNN-RNN model (PyTorch) for brainwave classification, integrated Neuphony FlexCap via pylsl, and implemented a Tkinter GUI with dynamic question flow using binary tree traversal.

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