Book title: Neural Oscillations in Neural Networks
Subtitle: Top neural networks that work best with EEG data and top EEG task classifications and data signals that work best with neural networks
Although quite short (~100 paperback pages or ~600 Kindle pages), it took me about a year to finish this book. This volume consolidates the existing body of knowledge, along with my own knowledge and experience in the space. It is quite straight to the point and easy to read.
The content of this volume covers the fundamentals of EEG, neural networks, and lastly the combination of the two, more specifically which neural networks architectures (deep neural networks) that work best with EEG data, along with which EEG task classifications we can analyze in neural networks as well as which type of EEG data works as the ‘right’ input data into the various neural networks architectures.
This book is geared towards a few types of readers:
If you are a student or someone who is curious about this space, this volume helps you save time learning the fundamentals of this space to help you make the right choices.
If you are an expert in EEG but have no background in neural networks, this volume should give you enough basic fundamentals of neural networks, more specifically which deep neural networks architectures work best with EEG data.
If you are an expert in neural networks but have no knowledge about EEG, this book should give you the fundamentals about EEG data, as well as where to find/get free EEG data, that are made available to the public via institutions or laboratories.
Below is the link to the Kindle and the paperback versions on Amazon and a quick intro video on my YouTube channels.
A few weeks ago I submitted the manuscript and the electronic version just went live on Kindle. Link below.
Book summary: Whether you are a student, an experienced neuroscientist, a computer scientist, or a curious individual, this book is designed to: 1) provide you with an overview of the fundamentals of neural oscillations (EEG or brainwaves), and neural networks (e.g. deep learning), and 2) cover the top deep neural network architectures that work best with neural oscillations/EEG data, top EEG classification tasks that work best with neural networks, and top EEG signals that work best as input data for neural networks. The main aim of this book is to provide you with the fundamentals needed to get you started. Detailed technical and programming specifics of these disciplines are beyond the scope of this book.
The paperback will launch too, once it gets approved. Feedback appreciated! #eeg#ml#dl#ai#deeplearning#neuroscience#book#kindlebooks#science#student#brain#mind#research
Book announcement: Launch on October 9, 2022. I wrote about this topic ~10 years ago as part of my thesis confirmation. It feels surreal to see that those thought experiments are now becoming part of the literature.
This manuscript covers the fundamentals that will hopefully help those interested in using the human brainwaves (EEG) data in deep learning architectures, such as convolutional neural networks for technical solutions and not just for the traditional EEG medical systems and solutions.
Manuscript submitted to the editor for publishing in 2 weeks.
High level table of content: EEG fundamentals Neural networks fundamentals Deep learning architectures for EEG