8
C.-H. Im
1.3.2 Event-Related Potential Analysis
In the history of EEG, the most important advancement was the use of stimulus-locked
averaging of event-related EEG. Using event-related potentials (ERP) analysis, one
can observe spatiotemporal components of stimulus-locked brain electrical activities
with reduced background noise. Examples of important ERP components include
P300 [20], N170 [4], mismatch negativity (MMN) [17], and error-related negativity
(ERN) [30], which have been widely used not only for cognitive/clinical neuroscience
studies [21] but also for BCI applications [7]. A series of methods has recently been
proposed to extract more precise spatiotemporal ERP waveforms with fewer repeated
trials, and this will be introduced in a detailed manner in Chap. 4.
1.3.3 EEG Source Imaging
The limited spatial resolution of EEG can be substantially enhanced by performing
EEG source imaging, or electrical source imaging (ESI), which estimates locations,
directions, and/or distribution of EEG sources by solving mathematically defined
problems called inverse problems [23]. To solve the inverse problems, a procedure for
modeling the human head and calculating the relationship between EEG sources and
scalp potentials is necessary. This procedure is generally referred to as forward calculation or solving forward problems. Because accurate forward calculation is important to obtain accurate inverse solutions, high-precision numerical methods, such as
the boundary element method (BEM) and finite-element method (FEM), have been
adopted. To solve the inverse problems, various algorithms and models have been
proposed, each of which has its own advantages and drawbacks. Detailed descriptions
of the methods for EEG forward/inverse problems can be found in Chap. 5.
1.3.4 Functional Connectivity Analysis
Traditional neuroscience studies focused on functional specification of brain areas;
however, recent neuroimaging studies exhibited increased interest in the functional
connectivity among different brain areas. EEG is especially useful to study functional connectivity between two recording sites (or brain areas after EEG source
imaging) because of its high temporal resolution. There are different kinds of functional connectivity measures that have been actively applied to EEG analyses, such
as coherence, phase-locking value (PLV), phase lag index (PLI), Granger’s causality (GC), and partial directed coherence (PDC). Functional connectivity analysis
has proved to be useful to characterize various psychiatric diseases. Indeed, several
recent studies have shown disrupted or abnormal functional connectivity patterns in
patients with psychiatric illnesses; examples include schizophrenia [27], mild cogni-
C.-H. Im
1.3.2 Event-Related Potential Analysis
In the history of EEG, the most important advancement was the use of stimulus-locked
averaging of event-related EEG. Using event-related potentials (ERP) analysis, one
can observe spatiotemporal components of stimulus-locked brain electrical activities
with reduced background noise. Examples of important ERP components include
P300 [20], N170 [4], mismatch negativity (MMN) [17], and error-related negativity
(ERN) [30], which have been widely used not only for cognitive/clinical neuroscience
studies [21] but also for BCI applications [7]. A series of methods has recently been
proposed to extract more precise spatiotemporal ERP waveforms with fewer repeated
trials, and this will be introduced in a detailed manner in Chap. 4.
1.3.3 EEG Source Imaging
The limited spatial resolution of EEG can be substantially enhanced by performing
EEG source imaging, or electrical source imaging (ESI), which estimates locations,
directions, and/or distribution of EEG sources by solving mathematically defined
problems called inverse problems [23]. To solve the inverse problems, a procedure for
modeling the human head and calculating the relationship between EEG sources and
scalp potentials is necessary. This procedure is generally referred to as forward calculation or solving forward problems. Because accurate forward calculation is important to obtain accurate inverse solutions, high-precision numerical methods, such as
the boundary element method (BEM) and finite-element method (FEM), have been
adopted. To solve the inverse problems, various algorithms and models have been
proposed, each of which has its own advantages and drawbacks. Detailed descriptions
of the methods for EEG forward/inverse problems can be found in Chap. 5.
1.3.4 Functional Connectivity Analysis
Traditional neuroscience studies focused on functional specification of brain areas;
however, recent neuroimaging studies exhibited increased interest in the functional
connectivity among different brain areas. EEG is especially useful to study functional connectivity between two recording sites (or brain areas after EEG source
imaging) because of its high temporal resolution. There are different kinds of functional connectivity measures that have been actively applied to EEG analyses, such
as coherence, phase-locking value (PLV), phase lag index (PLI), Granger’s causality (GC), and partial directed coherence (PDC). Functional connectivity analysis
has proved to be useful to characterize various psychiatric diseases. Indeed, several
recent studies have shown disrupted or abnormal functional connectivity patterns in
patients with psychiatric illnesses; examples include schizophrenia [27], mild cogni-