Jaeseung Jeong
Korea Advanced Institute of Science and Technology · 神経科学
研究室紹介
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.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15We 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
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
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
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.
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
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