Preface

Nowadays, there is significant scientific and industrial interest in developing a robust interface to connect the human brain to a computer-controlled system. The potential capabilities of such a system include a wide range of disability services, prosthetic organ control, and industrial and military applications. The aim of studies in brain-computer interfacing (BCI) is to provide a working tool for patients with disabilities to communicate neurophysiological activities into physical actions. The human-machine controller expands the degree of freedom and the available options for manipulation and navigation of the system by using direct cognitive commands.

This book presents some recently established methods for processing and deep learning methods for categorizing EEG signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems.

The book provides a comprehensive summary of conventional and novel methods for processing brain signals. These methods include some subcategories of deep learning methods to show the contribution of this methodology to BCI. It also provides an overview of the applications of BCI to highlight the growing idea of interfacing minds with machines. I hope this book will inspire academic readers and researchers with new ideas in this area. I would like to acknowledge the chapter authors for their excellent contributions.

> **Vahid Asadpour** Department of Research and Evaluation, Kaiser Permanente, Pasadena, California

Research Scientist (Former), UCLA, Los Angeles, California

Section 1
