Resources

This section provides additional resources and references for further reading on the topics covered in this documentation, including EEGs, its uses, Generative Adversarial Networks (GANs) and Artifact Subspace Reconstruction (ASR) in the context of EEG data analysis.

Below are literature and articles that can help you deepen your understanding of these subjects.

EEG Resources

Introduction to EEG and Digital Twins

• How To Interpret an EEG and its Report (Slidedeck)

• Digital twins and non-invasive recordings enable early diagnosis of Alzheimer's disease

• Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins

• Digital twin for personalized medicine development

• EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios

Introduction to Artifact Subspace Reconstruction (ASR)

• Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings

• The Artifact Subspace Reconstruction Method (Slidedeck)

Introduction to Generative Adversarial Networks (GANs)

• Generative adversarial networks in EEG analysis: an overview

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Tools and Libraries

Below are some useful tools and libraries for EEG data analysis, GAN implementation, and ASR.

EEG Data Analysis Libraries

• MNE-Python: A comprehensive library for processing and analyzing EEG and MEG data in Python.

• Pandas: A powerful data manipulation and analysis library for Python.

• NumPy: A fundamental package for scientific computing with Python, providing support for arrays and matrices.

• Matplotlib: A plotting library for creating static, animated, and interactive visualizations in Python.

GAN Libraries

• TensorFlow: An open-source machine learning framework that provides tools for building and training GANs.

• PyTorch: An open-source deep learning framework that offers dynamic computation graphs and is widely used for GAN implementations.

• EEGGAN: A Python library that provides implementations of various GAN architectures specifically designed for EEG data.

ASR Libraries

• ASRpy: A Python library for Artifact Subspace Reconstruction (ASR) in EEG data analysis.

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Tutorials and Guides

Here are some tutorials and guides to help you get started with EEG data analysis, GANs, and ASR.

• NumPy Beginner's Guide: A quickstart guide for beginners to learn NumPy.

• Pandas Documentation: Official documentation and getting started guide for Pandas.

• MNE-Python Tutorials: A collection of tutorials for using MNE-Python for EEG and MEG data analysis.

• GANs in TensorFlow: A tutorial on building and training GANs using TensorFlow.

• GANs in PyTorch: A tutorial on building and training GANs using PyTorch.