Prof. Chang-Hwan Im graduated from the School of Electrical Engineering of Seoul National University, Korea, in 1999. He received his MS and PhD from the same university in 2001 and 2005, respectively. He worked as a Post-doctor in the Department of Biomedical Engineering at the University of Minnesota, MN, USA, from 2005 to 2006. From 2006 to 2011, he worked for the Department of Biomedical Engineering at Yonsei University, Korea, as an Assistant/Associate Professor. Since 2011, he has been working for the Department of Biomedical Engineering at Hanyang University, Korea, as a Professor. His research interests cover various fields of computational neuroengineering, especially brain-computer interfaces, the diagnosis of neuropsychiatric diseases, noninvasive brain stimulation, bioelectromagnetic source imaging, neuromarketing, and dynamic neuroimaging. He has authored more than 150 articles in peer-reviewed international journals. He is currently a Director of the Computational Neuroengineering Laboratory of Hanyang University, and he is Associate Editors of Biomedical Engineering Letters, Experimental Neurobiology, and Brain-Computer Interfaces.

Provides an overview of the principles and applications of computational EEG analysis techniques

Describes cutting-edge techniques used for various applications including diagnosis of neurological disorder and psychiatric disease

Written by established world-class researchers developing state-of-the-art techniques

Part I. Introduction

Chapter 1. Basics of EEG: Generation, Acquisition and Applications of EEG ¿ Chang-Hwan Im

1.1. Generation of EEG

1.3. Computational EEG Analysis

1.4. Applications of EEG

Part II. Methods

Chapter 2. Preprocessing of EEG ¿ Sung-Phil Kim

2.1. Introduction

2.3. Artifact Removal

2.4. Discussion

Chapter 3. EEG Spectral Analysis ¿ Do-Won Kim and Chang-Hwan Im

3.1. Introduction

3.2. Methodological Background

3.3. Practical Remarks on EEG Spectral Analysis

Chapter 4. The Analysis of Event-Related Potentials ¿ Marco Congedo

4.1. Introduction

4.3. Time Domain Analysis;

4.4. Time-Frequency Domain Analysis

4.5. Spatial Domain Analysis

4.6. Inferential Statistics

4.7. Single-Sweep Classification

5.2. From the Scalp to the Brain ¿ the Inverse Problem

5.4. Conclusion

Chapter 6. Methods for Functional Connectivity Analysis ¿ Jeong-Woo Choi and Kyung-Hwan Kim

6.1. Introduction

6.2. Procedure for the FCA

6.3. FC Metrics

6.4. Volume Conduction Problem

6.5. EEG FC Analysis on Source Space

6.6. Determination of Significance

Part III. Applications

Chapter 7. Computational EEG Analysis for the Diagnosis of Psychiatric Illnesses ¿ Seung-Hwan Lee and Yeonsoo Park

7.2. Promising EEG Biomarker Candidates

7.3. Psychiatric Disorders and EEG Biomarkers

7.4. Diagnosis for Cognitive impairment: Dementia and Minimal Cognitive Impairment

7.5. Conclusion

8.2. EEG Source Imaging

8.3. High Frequency Oscillation (HFOs)

Chapter 9. Computational EEG Analysis for Brain-Computer Interfaces ¿ Garett D. Johnson and Dean J. Krusienski

9.1 Brain-Computer Interfaces

9.2. Active Paradigms

9.3. Reactive Paradigms

9.4. Other Considerations

10.2. Hyperlink for social perception

10.3. Hyperlink for social interaction

10.4. Future perspectives