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S.-H. Lee and Y. Park
promising approach with high potential in measuring the pathologies of mental illnesses. It possesses several crucial merits, such as high temporal resolution, relatively
low economic burden, and easy accessibility compared to other neuroimaging methods [e.g., magnetic resonance imaging (MRI), positron-emission tomography (PET),
and magnetoencephalography (MEG)].
During the past few decades, the EEG analysis methods have rapidly evolved in
line with the advancement of technology in fields such as engineering and physics.
While EEG analysis at the electrode level (surface of scalp) had critical limitations
as neuronal signals of the brain would blur or fade as they reached the scalp, the
development of source level analysis methods has brought an evolution by allowing signals to represent the characteristics of the cortical level. This evolution has
enabled researchers to conduct more sophisticated studies that inspect region specific biomarkers, because the source level analysis could use the information of
brain anatomical region. Currently, we are at the midpoint of developing a variety
of future biomarkers that accurately portray pathological features. During this stage,
the development of EEG biomarkers is expected to play a pivotal role.
In this chapter, we will first review the candidate EEG biomarkers that have
been studied in relations to various psychiatric disorders. General characteristics of
these EEG biomarkers will be introduced. In the following section, we will look
into specific disorders and discuss how the previously mentioned EEG biomarkers
manifest to make them reliable markers of specific mental illnesses.
7.2 Promising EEG Biomarker Candidates
7.2.1 P300
P300 is the most widely studied event-related potential (ERP) component reflecting brain functions such as attention, working memory, and cognitive decline. It
appears most ostensibly at the centro-parietal scalp areas and is usually elicited
within 250–500 ms after the stimulus presentation. There are unique distinctions
between two regionally dependent P3 components, specifically the frontally maximal P3a component and the parietally maximal P3b component. The P3a component
is triggered by infrequent distinct tones presented within a series of frequent tones
during a resting state. The P3b component is a task-relevant potential elicited during
target stimulus processing. The P3a component has been associated with attention
mechanisms and novel stimulus processing, while P3b has been more related to
stimulus evaluation and decision making [111]. When experts of neurophysiology
mention the P3 component, they usually refer to the P3b [84, 111].
Because of its reflections of cognitive functioning, P3 components have been
widely studied in respect to psychiatric illnesses that are characterized by cognitive impairments. Although most studies have been confined to the electrode level,
S.-H. Lee and Y. Park
promising approach with high potential in measuring the pathologies of mental illnesses. It possesses several crucial merits, such as high temporal resolution, relatively
low economic burden, and easy accessibility compared to other neuroimaging methods [e.g., magnetic resonance imaging (MRI), positron-emission tomography (PET),
and magnetoencephalography (MEG)].
During the past few decades, the EEG analysis methods have rapidly evolved in
line with the advancement of technology in fields such as engineering and physics.
While EEG analysis at the electrode level (surface of scalp) had critical limitations
as neuronal signals of the brain would blur or fade as they reached the scalp, the
development of source level analysis methods has brought an evolution by allowing signals to represent the characteristics of the cortical level. This evolution has
enabled researchers to conduct more sophisticated studies that inspect region specific biomarkers, because the source level analysis could use the information of
brain anatomical region. Currently, we are at the midpoint of developing a variety
of future biomarkers that accurately portray pathological features. During this stage,
the development of EEG biomarkers is expected to play a pivotal role.
In this chapter, we will first review the candidate EEG biomarkers that have
been studied in relations to various psychiatric disorders. General characteristics of
these EEG biomarkers will be introduced. In the following section, we will look
into specific disorders and discuss how the previously mentioned EEG biomarkers
manifest to make them reliable markers of specific mental illnesses.
7.2 Promising EEG Biomarker Candidates
7.2.1 P300
P300 is the most widely studied event-related potential (ERP) component reflecting brain functions such as attention, working memory, and cognitive decline. It
appears most ostensibly at the centro-parietal scalp areas and is usually elicited
within 250–500 ms after the stimulus presentation. There are unique distinctions
between two regionally dependent P3 components, specifically the frontally maximal P3a component and the parietally maximal P3b component. The P3a component
is triggered by infrequent distinct tones presented within a series of frequent tones
during a resting state. The P3b component is a task-relevant potential elicited during
target stimulus processing. The P3a component has been associated with attention
mechanisms and novel stimulus processing, while P3b has been more related to
stimulus evaluation and decision making [111]. When experts of neurophysiology
mention the P3 component, they usually refer to the P3b [84, 111].
Because of its reflections of cognitive functioning, P3 components have been
widely studied in respect to psychiatric illnesses that are characterized by cognitive impairments. Although most studies have been confined to the electrode level,