Brain-Computer Interface (BCI) Project

Table of Contents

• Introduction
• Features
• Getting Started
  • Requirements
  • Installation
  • Usage
• Contributing
• License

Introduction

Welcome to the Brain-Computer Interface (BCI) project! This repository contains the source code and documentation for our BCI system, which allows users to interact with computers or other devices using their brain signals. The project aims to provide a user-friendly and versatile interface to enable various applications in the fields of assistive technology, neuroscience, and human-computer interaction.

Features

• Real-time Signal Acquisition: The BCI system is capable of acquiring brain signals in real-time using EEG (Electroencephalogram) sensors.
• Signal Preprocessing: Preprocess the raw EEG data to improve signal quality and remove noise using various filtering techniques.
• Machine Learning Models: Employ advanced machine learning algorithms to classify brain signals and translate them into meaningful commands or actions.

Getting Started

To use the BCI system on your local machine, follow these instructions:

Requirements

• EEG hardware and electrodes compatible with the system. (Emotiv BCI Headset)
• Python 3.x installed on your system.

Installation

1. Clone this repository to your local machine.
2. Install the required Python packages by running the following command:

   pip install -r requirements.txt
   

Usage

1. Connect Emotiv BCI Headset
   • Ensure you have the Emotiv BCI headset and the necessary electrodes.
   • Follow the manufacturer's instructions to properly set up and connect the headset to your computer.
2. Run Data Acquisition Script
   • Use the provided data acquisition script to collect EEG data from the Emotiv BCI headset.
   • Execute the script to start the data collection process.
3. Train and Run Machine Learning Models
   • Utilize the preprocessed EEG data to train machine learning models for classification.
   • Depending on your project, choose appropriate Models such as TCNN, EEGNET, SSVEPNET, etc.
   • Use the trained models to interpret brain signals and convert them into meaningful commands or actions.

Contributing

Contributions to this project are welcome! If you find any issues or have suggestions for improvements, feel free to open a GitHub Issue or submit a pull request.

License

This project is licensed under the MIT License. Feel free to use and modify the code for your needs while respecting the terms of the license.