Welcome to the EEG Deep Learning Library, also known as EEG-DL. This powerful library is designed for classifying EEG (Electroencephalogram) signals using advanced deep learning algorithms powered by TensorFlow. In this tutorial, we will walk you through the setup, usage, and common troubleshooting techniques to ensure a smooth experience with EEG-DL.

Table of Contents

• Documentation
• Usage Demo
• Common Issues
• Other Useful Resources

Documentation

The EEG-DL library supports a range of models that cater to various classification needs. Here’s a condensed list of some of the models available:

• Deep Neural Networks: [DNN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/DNN.py)
• Convolutional Neural Networks: [CNN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/CNN.py)
• Recurrent Neural Networks: [RNN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/RNN.py)
• Transformer: [Transformer](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/main-Transformer.py)

Usage Demo

To kick off your journey with EEG-DL, follow these straightforward steps:

1. Download the EEG Motor Movement Imagery Dataset using the Python script:

$ python MIND_Get_EDF.py

2. Read the .edf files in a Python 2.7 environment and convert them into .m files using:

$ python Extract-Raw-Data-Into-Matlab-Files.py

3. Preprocess the dataset using appropriate Matlab scripts to generate Excel files.
4. Train and test the models under a recommended Python 3.6 environment:

$ pip install --upgrade --force-reinstall tensorflow-gpu==1.13.1 --user

5. Utilize TensorBoard to read the evaluation criteria after your model has been trained.

Common Issues

Every project comes with its share of hiccups. Here’s a list of common issues and how to tackle them:

1. ValueError: Ensure label shapes are consistent. Use np.squeeze to modify the shape effectively.
2. InvalidArgumentError: For issues related to histograms in gradients, simply comment out the histogram code within the respective model file.
3. TypeError: Adjust the coarsening level settings in the GCN model file as needed. This is essential for configuring the model correctly.
4. General Compatibility Issues: Double-check Python environments. For optimal results, Python 2.7 is mandatory, and specific library versions should be maintained.
5. Label Values Out of Range: Ensure that your labels start from 0 for compatibility with your GCN model.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Other Useful Resources

For diving deeper into the world of deep learning and EEG applications, here are a few recommended resources:

• Python Environment Setup Tutorial
• TensorFlow for Deep Learning Tutorial

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Happy coding!