Preface
The electroencephalogram (EEG), a recording of electrical activities in the brain, is
becoming an indispensable tool to investigate human brain functions and to diagnose various psychiatric and neurological disorders. Since the first recording of a
human EEG by Dr. Hans Berger, a psychiatrist, in 1924, the development of EEG
technology has continued, and, increasingly, this technology has drawn interest
from researchers in various disciplines, including clinicians, neuroscientists, psychologists, and biomedical engineers. Owing to recent advances in digital technology and software methodology, EEG is now being used as an important tool in
numerous fields, such as cognitive neuroscience, neuromarketing, neuroergonomics, brain–computer interfaces, neurofeedback, and sports science.
Although there is a consensus that EEGs are easier to record than other
brain-imaging techniques, such as functional magnetic resonance imaging and
positron emission tomography, the analysis of EEGs is not straightforward. For
example, if one wants to observe functional connectivity between two brain regions
of interest, it is necessary to perform a series of EEG processing steps, including
pre-processing, EEG source imaging, and functional connectivity analysis.
Although there are several software packages offering comprehensive tools for
advanced EEG analyses, users still need to choose the specific computational EEG
analysis methods most appropriate for their EEG data. Indeed, there are many kinds
of methods for computational EEG analysis, e.g., a variety of functional connectivity measures. Therefore, it is recommended that EEG researchers understand the
detailed theoretical background of the computational EEG analysis methods being
used. Knowledge of the advantages and disadvantages of each method would help
researchers achieve more successful EEG analysis results.
In this book, we intend to provide a comprehensive review of the state-of-the-art
methods for computational EEG analysis. This book is not a handbook, but a
textbook written by multiple experts. Therefore, this book should be useful not only
to biomedical engineers who are in the initial stages of working on the development
v
The electroencephalogram (EEG), a recording of electrical activities in the brain, is
becoming an indispensable tool to investigate human brain functions and to diagnose various psychiatric and neurological disorders. Since the first recording of a
human EEG by Dr. Hans Berger, a psychiatrist, in 1924, the development of EEG
technology has continued, and, increasingly, this technology has drawn interest
from researchers in various disciplines, including clinicians, neuroscientists, psychologists, and biomedical engineers. Owing to recent advances in digital technology and software methodology, EEG is now being used as an important tool in
numerous fields, such as cognitive neuroscience, neuromarketing, neuroergonomics, brain–computer interfaces, neurofeedback, and sports science.
Although there is a consensus that EEGs are easier to record than other
brain-imaging techniques, such as functional magnetic resonance imaging and
positron emission tomography, the analysis of EEGs is not straightforward. For
example, if one wants to observe functional connectivity between two brain regions
of interest, it is necessary to perform a series of EEG processing steps, including
pre-processing, EEG source imaging, and functional connectivity analysis.
Although there are several software packages offering comprehensive tools for
advanced EEG analyses, users still need to choose the specific computational EEG
analysis methods most appropriate for their EEG data. Indeed, there are many kinds
of methods for computational EEG analysis, e.g., a variety of functional connectivity measures. Therefore, it is recommended that EEG researchers understand the
detailed theoretical background of the computational EEG analysis methods being
used. Knowledge of the advantages and disadvantages of each method would help
researchers achieve more successful EEG analysis results.
In this book, we intend to provide a comprehensive review of the state-of-the-art
methods for computational EEG analysis. This book is not a handbook, but a
textbook written by multiple experts. Therefore, this book should be useful not only
to biomedical engineers who are in the initial stages of working on the development
v