Disclaimer: All work/ideas shared on my personal social media channels are my own and are independent of any institution and.or industry.
Science is a never-ending exploration and evolution of thoughts and ideas. The first time that I ever publicly talked about exploring opportunities around neural oscillations in neural networks was ~2012. While it is taking longer than anticipated, the exploration continues.
This video covers a high level discussion about one way in which EEGs (a.k.a brainwaves/neural oscillations) can be used as a data input in convolutional neural networks to help read sentiments/expressions. I also briefly talk about Deepface (a “lightweight face recognition and facial attribute analysis – age, gender, emotion and race – framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace, Dlib and SFace.”) to hone in on the capabilities of existing neural network models that can read facial images.
While Deepface can help read facial images, we can now use the same models, such as convolutional neural networks, to read brainwave images using the same types of architecture. In that sense, we are expanding our capabilities from facial to brain images. Exploring these technologies hold great future and if you are a student, and interested in these topics, I highly recommend exploring this space.
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