Chapter 7
Computational EEG Analysis for the
Diagnosis of Psychiatric Illnesses
Seung-Hwan Lee and Yeonsoo Park
Abstract Electroencephalography (EEG) holds promise as a tool to diagnose psychiatric disorders. While it has some major advantages such as high temporal resolution, relative affordability, and easy accessibility, even its shortcomings are being
addressed through the advancement in its analysis. As a result, numerous researches
have been examining EEG components as potential biomarkers of various psychiatric diseases. In this chapter, we discuss several promising EEG markers, ranging
from resting state EEG to stimuli induced ERP components, from electrodes level
to source level, and from band power to functional connectivity networks. In addition, we present the findings of previous studies with an emphasis on how each EEG
component vary depending of the specific psychiatric illnesses. The psychiatric disorders discussed in this chapter are (1) schizophrenia, (2) bipolar disorder, (3) major
depressive disorder, (4) anxiety related disorders (e.g., post-traumatic stress disorder
and obsessive compulsive disorder) and (5) disorders related to cognitive impairments (e.g., dementia and minimal cognitive impairment. Lastly, we introduce how
the limitations of EEG, which mostly occur as a byproduct of sensor-level analysis,
can be addressed through source-level analysis.
7.1 Introduction
Traditionally, psychiatric disorders have been diagnosed primarily through faceto-face interviews, and biological measures that could effectively capture clinical
features of mental illnesses were relatively scant. To overcome such unmet needs,
many researchers placed meticulous effort into developing biomarkers that could
reliably detect the clinical characteristics of specific psychopathologies or psychiatric illnesses. Among the various biomarkers, electroencephalography (EEG) is a
S.-H. Lee (B)
Department of Psychiatry, Inje University, Ilsan-Paik Hospital, Goyang, Republic of Korea
e-mail: lshpss@paik.ac.kr
S.-H. Lee · Y. Park
Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, South Korea
© Springer Nature Singapore Pte Ltd. 2018
C.-H. Im (ed.), Computational EEG Analysis, Biological and Medical Physics,
Biomedical Engineering, https://doi.org/10.1007/978-981-13-0908-3_7
149
Computational EEG Analysis for the
Diagnosis of Psychiatric Illnesses
Seung-Hwan Lee and Yeonsoo Park
Abstract Electroencephalography (EEG) holds promise as a tool to diagnose psychiatric disorders. While it has some major advantages such as high temporal resolution, relative affordability, and easy accessibility, even its shortcomings are being
addressed through the advancement in its analysis. As a result, numerous researches
have been examining EEG components as potential biomarkers of various psychiatric diseases. In this chapter, we discuss several promising EEG markers, ranging
from resting state EEG to stimuli induced ERP components, from electrodes level
to source level, and from band power to functional connectivity networks. In addition, we present the findings of previous studies with an emphasis on how each EEG
component vary depending of the specific psychiatric illnesses. The psychiatric disorders discussed in this chapter are (1) schizophrenia, (2) bipolar disorder, (3) major
depressive disorder, (4) anxiety related disorders (e.g., post-traumatic stress disorder
and obsessive compulsive disorder) and (5) disorders related to cognitive impairments (e.g., dementia and minimal cognitive impairment. Lastly, we introduce how
the limitations of EEG, which mostly occur as a byproduct of sensor-level analysis,
can be addressed through source-level analysis.
7.1 Introduction
Traditionally, psychiatric disorders have been diagnosed primarily through faceto-face interviews, and biological measures that could effectively capture clinical
features of mental illnesses were relatively scant. To overcome such unmet needs,
many researchers placed meticulous effort into developing biomarkers that could
reliably detect the clinical characteristics of specific psychopathologies or psychiatric illnesses. Among the various biomarkers, electroencephalography (EEG) is a
S.-H. Lee (B)
Department of Psychiatry, Inje University, Ilsan-Paik Hospital, Goyang, Republic of Korea
e-mail: lshpss@paik.ac.kr
S.-H. Lee · Y. Park
Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, South Korea
© Springer Nature Singapore Pte Ltd. 2018
C.-H. Im (ed.), Computational EEG Analysis, Biological and Medical Physics,
Biomedical Engineering, https://doi.org/10.1007/978-981-13-0908-3_7
149