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Jaeseung Jeong

Korea Advanced Institute of Science and Technology · Neuroscience

About the Lab

Professor Jaeseung Jeong's research lab specializes in nonlinear dynamics and complex systems analysis applied to neurological and physiological data. The lab focuses on understanding brain network organization, particularly in neurodegenerative diseases such as Alzheimer’s and Parkinson’s, using advanced signal processing techniques like wavelet analysis, correlation dimension estimation, and community detection in neural connectomes. A key research direction involves distinguishing neurological disorders through subtle changes in EEG dynamics, emphasizing the detection of determinism and complexity in short, noisy time series. The lab also investigates the functional role of spontaneous behaviors like eyeblinking in cognitive processing, linking them to underlying brain dynamics.

nonlinear dynamicsEEG analysisneural networksbrain connectivityneurodegenerative diseases

Research Overview

Papers
232
Total Citations
6,555
Papers (5y)
23
Primary Field
Neuroscience

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
23total
2022
2023
2024
2025
2026
Citations per year (5y)
81total
20222023202420252026

Selected Papers

15
1
Review|1,302 citations·2004
EEG dynamics in patients with Alzheimer's disease
Jaeseung Jeong
SJR Q1Clinical Neurophysiology
Cognitive NeuroscienceNeuroscience
2
Article|393 citations·2001
Mutual information analysis of the EEG in patients with Alzheimer's disease
Jaeseung Jeong, John C. Gore, Bradley S. Peterson
SJR Q1Clinical Neurophysiology
Cognitive NeuroscienceNeuroscience
3
Article|160 citations·2019
Neural circuits underlying a psychotherapeutic regimen for fear disorders
Jinhee Baek, Sukchan Lee, Taesup Cho, Seong-Wook Kim, Minsoo Kim, Yongwoo Yoon, Ko Keun Kim, Junweon Byun, Sang Jeong Kim, Jaeseung Jeong, Hee‐Sup Shin
SJR Q1Nature
Cognitive NeuroscienceNeuroscience
4
Article|139 citations·1998
Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension
Jaeseung Jeong, Soo Yong Kim, S. Duke Han
Electroencephalography and Clinical Neurophysiology
Molecular BiologyBiochemistry, Genetics and Molecular Biology
5
Article|110 citations·2017
Neural dynamics of two players when using nonverbal cues to gauge intentions to cooperate during the Prisoner's Dilemma Game
Jaehwan Jahng, Jerald D. Kralik, Dong‐Uk Hwang, Jaeseung Jeong
SJR Q1NeuroImageOA
Cognitive NeuroscienceNeuroscience
6
Article|91 citations·2012
The timing and temporal patterns of eye blinking are dynamically modulated by attention
Jihoon Oh, Soyeong Jeong, Jaeseung Jeong
SJR Q2Human Movement Science
Cognitive NeuroscienceNeuroscience
7
Article|85 citations·2006
Alterations in cerebral perfusion in posttraumatic stress disorder patients without re-exposure to accident-related stimuli
Yong An Chung, Sung Hoon Kim, Soo Kyo Chung, Jeong‐Ho Chae, Dong Yang, Hyung Sun Sohn, Jaeseung Jeong
SJR Q1Clinical Neurophysiology
Clinical PsychologyPsychology
8
Article|84 citations·1998
Nonlinear analysis of the EEG of schizophrenics with optimal embedding dimension
Jaeseung Jeong, Dai‐Jin Kim, Jeong‐Ho Chae, Soo Yong Kim, Hyo-Jin Ko, In‐Ho Paik
SJR Q3Medical Engineering & Physics

We estimated the correlation dimensions of EEGs in patients with schizophrenia to investigate the dynamical properties underlying the EEG. We employed a new method, proposed by Kennel et al. (Kennel MB, Brown R, Abarbanel HDI. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 1992;45:3403-11), to calculate the correlation dimension D2. That method determined the proper minimum embedding dimension by looking at the behaviour of nearest nei

Cognitive NeuroscienceNeuroscience
9
Article|80 citations·2011
Topological Cluster Analysis Reveals the Systemic Organization of the Caenorhabditis elegans Connectome
Yunkyu Sohn, Myung-kyu Choi, Yong‐Yeol Ahn, Junho Lee, Jaeseung Jeong
SJR Q1PLoS Computational BiologyOA

The modular organization of networks of individual neurons interwoven through synapses has not been fully explored due to the incredible complexity of the connectivity architecture. Here we use the modularity-based community detection method for directed, weighted networks to examine hierarchically organized modules in the complete wiring diagram (connectome) of Caenorhabditis elegans (C. elegans) and to investigate their topological properties. Incorporating bilateral symmetry of the network as

AgingBiochemistry, Genetics and Molecular Biology
10
Article|73 citations·2012
Multiway array decomposition analysis of EEGs in Alzheimer's disease
Charles-Francois V. Latchoumane, François Vialatte, Jordi Solé‐Casals, Monique Maurice, Sunil Wimalaratna, Nigel Hudson, Jaeseung Jeong, Andrzej Cichocki
SJR Q3Journal of Neuroscience MethodsOA
Signal ProcessingComputer Science
11
Article|69 citations·2015
Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease
Dong‐Hwa Jeong, Young‐Do Kim, In‐Uk Song, Yong‐An Chung, Jaeseung Jeong
SJR Q2EntropyOA

Parkinson’s disease (PD) and Alzheimer’s disease (AD) can coexist in severely affected; elderly patients. Since they have different pathological causes and lesions and consequently require different treatments; it is critical to distinguish PD-related dementia (PD-D) from AD. Conventional electroencephalograph (EEG) analysis has produced poor results. This study investigated the possibility of using relative wavelet energy (RWE) and wavelet coherence (WC) analysis to distinguish between PD-D pat

Cognitive NeuroscienceNeuroscience
12
Article|65 citations·2002
Nonlinear dynamics of EEG in Alzheimer's disease
Jaeseung Jeong
SJR Q2Drug Development Research

Abstract Nonlinear dynamical analysis has been widely applied to a variety of physiological data for last two decades. One of its major contributions is to the electroencephalogram (EEG) in Alzheimer's disease (AD). A number of studies using nonlinear dynamical methods have shown the globally decreased complexity of EEG patterns in AD patients. A prominent decrease in information transmission among cortical areas quantified by information‐theoretic measures like mutual information is also found.

Cognitive NeuroscienceNeuroscience
13
Article|63 citations·2012
Spontaneous Eyeblinks Are Correlated with Responses during the Stroop Task
Jihoon Oh, Mookyung Han, Bradley S. Peterson, Jaeseung Jeong
SJR Q1PLoS ONEOA

The timing and frequency of spontaneous eyeblinking is thought to be influenced by ongoing internal cognitive or neurophysiological processes, but how precisely these processes influence the dynamics of eyeblinking is still unclear. This study aimed to better understand the functional role of eyeblinking during cognitive processes by investigating the temporal pattern of eyeblinks during the performance of attentional tasks. The timing of spontaneous eyeblinks was recorded from 28 healthy subjec

Public Health, Environmental and Occupational HealthMedicine
14
Article|59 citations·2002
A method for determinism in short time series, and its application to stationary EEG
Jaeseung Jeong, John C. Gore, Bradley S. Peterson
SJR Q1IEEE Transactions on Biomedical Engineering

A novel method for detecting determinism in short time series is developed and applied to investigate determinism in stationary electroencephalogram (EEG) recordings. This method is based on the observation that the trajectory of a time series generated from a differentiable dynamical system behaves smoothly in an embedded state space. The angles between two successive tangent vectors in the trajectory reconstructed from the time series is calculated as a function of time. The irregularity of th

Cognitive NeuroscienceNeuroscience
15
Article|54 citations·2004
Dimensional complexity of the EEG in patients with posttraumatic stress disorder
Jeong‐Ho Chae, Jaeseung Jeong, Bradley S. Peterson, Dai‐Jin Kim, Won‐Myong Bahk, Tae‐Youn Jun, Soo-Yong Kim, Kwang‐Soo Kim
SJR Q1Psychiatry Research Neuroimaging
Cognitive NeuroscienceNeuroscience

Research Areas

Cognitive NeuroscienceSocial PsychologyArtificial IntelligencePsychiatry and Mental healthCellular and Molecular NeuroscienceSociology and Political Science

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