https://github.com/enansari/bachelor-final-project

This repository contains my final bachelor project. This project includes the presentation and explanation of an article about converting thoughts into text
https://github.com/enansari/bachelor-final-project

artificial-intelligence autoencoder cnn eeg eeg-analysis eeg-signals hakim-sabzevari-university hsu lstm neural-network new-south-wales rnn signal-processing xgboost

Last synced: about 2 months ago
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This repository contains my final bachelor project. This project includes the presentation and explanation of an article about converting thoughts into text

• Host: GitHub
• URL: https://github.com/enansari/bachelor-final-project
• Owner: EnAnsari
• License: mit
• Created: 2024-11-04T09:10:06.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-04T10:06:07.000Z (about 2 months ago)
• Last Synced: 2024-11-04T10:28:53.745Z (about 2 months ago)
• Topics: artificial-intelligence, autoencoder, cnn, eeg, eeg-analysis, eeg-signals, hakim-sabzevari-university, hsu, lstm, neural-network, new-south-wales, rnn, signal-processing, xgboost
• Homepage: https://paperswithcode.com/paper/converting-your-thoughts-to-texts-enabling
• Size: 28.3 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Converting Your Thoughts to Text Using EEG Signals

### Overview

This repository contains research and resources for converting thoughts to text using EEG signals. This project explores the use of EEG-based brain-computer interfaces (BCIs) that leverage deep learning models to interpret motor imagery (MI) EEG signals. By applying neural networks, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), we aim to create a practical system that can interpret specific mental tasks and translate them into typing commands.

### Table of Contents

+ [Overview](https://github.com/EnAnsari/Bachelor-final-project/tree/main?tab=readme-ov-file#overview)

+ [Download Links](https://github.com/EnAnsari/Bachelor-final-project/tree/main?tab=readme-ov-file#Download-Links)

+ [Related Pages](https://github.com/EnAnsari/Bachelor-final-project/tree/main?tab=readme-ov-file#Related-Pages)

+ [Additional Notes](https://github.com/EnAnsari/Bachelor-final-project/tree/main?tab=readme-ov-file#Additional-Notes)

### The repository is organized as follows:

+ **Research Paper**: A detailed paper explaining the proposed methods, experiments, and results.

+ **Model Files**: Pre-trained models and code for CNN and RNN architectures used to analyze EEG signals.

+ **Data Processing Scripts**: Code for processing raw EEG signals to prepare data for model training and testing.

+ **Documentation**: Supporting documentation and reports related to the research and development process.

Each section contains additional details and files relevant to the process of converting EEG signals into interpretable text or commands.

### Download Links

Here you can add links to download datasets, pre-trained models, or additional resources required to run the project.

| File Name | Size |download link|

|---|:---:|:---:|

| original paper | 2.11MB | [download](https://github.com/EnAnsari/Bachelor-final-project/releases/download/dl/converting-your-thoughts-to-text.pdf) |

| document | 1.0MB | [download](https://github.com/EnAnsari/Bachelor-final-project/releases/download/dl/doc.pdf) |

*Note: Please make sure to adhere to the licensing requirements for any external datasets used in the project.*

### Related Pages

+ **Project Background**: Background information and context for the BCI project.

+ **System Architecture**: A breakdown of the neural network architecture and data pipeline.

+ **Experiments and Results**: A detailed report of experimental findings, model performance, and evaluation metrics.

+ **Usage and Deployment**: Instructions on setting up and deploying the BCI system.

### Additional Notes

+ **Adding Persian Content**: You can create a separate README_FA.md file for the Persian version of this content.

+ **Linking Pages in the Repository**: Replace placeholders with the actual paths or URLs of the corresponding pages within your repository.

+ **Download Links**: Make sure the files are accessible to users, either via GitHub releases, cloud storage, or other downloadable sources.

+ This structure keeps the information clear and accessible, helping users easily navigate the resources and understand the project goals.