NeuroLinkAI leverages advanced deep learning technologies, including Long Short-Term Memory (LSTM) networks, to interpret and translate brain signals into actionable commands for controlling neuroprosthetic devices. This project aims to enhance autonomy for individuals with mobility impairments due to spinal cord injuries, strokes, and similar conditions.
- Brain-Computer Interface (BCI): Converts EEG signals into direct commands for neuroprosthetics.
- Deep Learning: Uses LSTM networks for accurate and real-time interpretation of neural signals.
- Accessibility: Designed to improve the quality of life for individuals with severe mobility disabilities.
To set up the NeuroLinkAI system, follow these steps:
- Clone the repository:
git clone [https://github.com/Rkpani05/NeuroLinkAi-Deep_Learning_Driven_Neuroprosthetic_Control_Interface.git] - Install required Python packages:
pip install -r requirements.txt - Run the application:
python app.py
- Ensure that a compatible EEG headset is properly connected.
- Run the system to begin signal acquisition and real-time processing.
NeuroLinkAI includes several key components:
- EEG Signal Acquisition: Captures brain signals using non-invasive EEG headsets.
- Preprocessing Module: Filters and standardizes EEG signals.
- Feature Extraction Module: Extracts significant features from EEG data.
- Deep Learning Module: Analyzes features to predict user intentions.
- Control Interface: Translates predictions into prosthetic commands.
- Python: For overall programming.
- TensorFlow and Keras: For implementing LSTM networks.
- MNE-Python: For EEG signal processing.
Contributions to NeuroLinkAI are welcome! Please read report for details on our code of conduct and the process for submitting pull requests.
This project is licensed under the MIT License.
- Rohit Kumar Pani ([https://github.com/Rkpani05])
- Contributors and researchers in the BCI field.