Meet the Volume Editor

Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In

addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.

Contents

**Section 1**

*by İbrahim Kaya*

Fuzzy Inference System

Machine Learning Algorithms

*by Jen A. Markovics*

**Section 2**

**Preface XV**

Introduction to Brain-Computer Interface **1**

**Chapter 1 3**

**Chapter 2 23**

Therapeutic Brain-Computer Interfacing **41**

**Chapter 3 43**

**Chapter 4 53**

**Chapter 5 75**

**Chapter 6 93**

Language as the Working Model of Human Mind

A Brief Summary of EEG Artifact Handling

*and Mohammad-Reza Akbarzadeh-Totonchi*

*by Ilan Figueirêdo, Lílian Lefol Nani Guarieiro and Erick Giovani Sperandio Nascimento*

*Lubaina Jetaji and Shubha Dube*

*by Amitabh Dube, Umesh Kumar, Kapil Gupta, Jitendra Gupta, Bhoopendra Patel, Sanjay Kumar Singhal, Kavita Yadav,* 

Pain Identification in Electroencephalography Signal Using

Multivariate Real Time Series Data Using Six Unsupervised

Therapeutic Effect of Infra-Low-Frequency Neurofeedback

Training the Conductor of the Brainwave Symphony: In Search of a Common Mechanism of Action for All Methods of Neurofeedback

Training on Children and Adolescents with ADHD *by Horst Schneider, Jennifer Riederle and Sigrid Seuss*

*by Vahid Asadpour, Reza Fazel-Rezai, Maryam Vatankhah* 

## Contents



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

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

**Vahid Asadpour**

UCLA,

Kaiser Permanente, Pasadena, California

Research Scientist (Former),

Los Angeles, California

Department of Research and Evaluation,

commands.

authors for their excellent contributions.
