arXiv subjects

Youngjai Park

Publications and source records attributed to Youngjai Park.

4 recordsLinked to original sources

arXiv2026

Human brain state classification via permutation entropy of EEG phase dynamics across consciousness levels and inattentive-type ADHD

We analyze electroencephalography (EEG) signals using the ordinal pattern framework to investigate whether different human brain states can be distinguished based on the disorder of EEG dynamics. Rather than analyzing raw EEG signals, we focus on the principal mode of EEG phase dynamics, reflecting anterior-posterior information flow, and quantify disorder using permutation entropy. We apply this to two datasets: (i) EEG recordings from a general anesthesia protocol, and (ii) EEG recordings acquired in the resting state from healthy control subjects and individuals with inattentive-type attention deficit hyperactivity disorder (ADHD), including eyes-open and eyes-closed conditions. We find that the permutation entropy distributions exhibit a clear dependence on brain state. In particular, conscious, inattentive-type ADHD, and eyes closed conditions show lower mean values and larger standard deviations of permutation entropy. To evaluate the discriminative power of permutation entropy, we train classification models using permutation entropy as an input feature. The results show that the distinction between conscious and unconscious states can be reliably captured in the general-anesthesia dataset. In the resting-state dataset, eyes-open and eyes-closed conditions are distinguishable, whereas classification between control and inattentive-type ADHD groups does not show clear separability. This indicates that information not captured in ordinal patterns, such as the original time-series values, may play a more crucial role in detecting inattentive-type ADHD. Our findings demonstrate that permutation entropy derived from EEG phase dynamics provides an effective indicator of brain states, particularly in relation to consciousness, while also highlighting its limitations for identifying individuals with inattentive-type ADHD.

q-bio.NC↗

arXiv2026

Structure of Pitch-Pattern Motifs in Major League Baseball

Baseball consists of two teams alternating between batting and fielding while competing to score runs through sequential pitching events. Recent advances in tracking technology have enabled all Major League Baseball (MLB) clubs to record every pitch with high resolution, yet most quantitative studies have primarily emphasized single-pitch metrics, leaving the role of sequential structure less explored. Here, we examine pitch-pattern motifs of multiple lengths using approximately 12.4 million Statcast pitch recordings from the 2008-2025 MLB regular seasons at two complementary scales. At the macroscale, we quantify pitch-sequence diversity using the Shannon entropy and inverse Simpson index and examine their relationships with earned run average and wins. At the microscale, we compare hit and out frequencies across pitch-pattern motifs. Rather than identifying outcome-determining sequences, we find that motif usage exhibits stable, non-random organization, as reflected in Zipf's and Heaps' laws, while showing limited association with conventional performance measures. While language-like scaling (Zipf's and Heaps' laws) clearly reveals an underlying 'grammar' of MLB pitch sequences, that grammar alone is insufficient to account for performance indicators such as earned run average or wins. These results suggest that sequence-based analyses clarify the structural organization of pitch usage, while also delineating the limits of motif-based approaches for explaining performance without richer contextual information.

physics.soc-ph↗

arXiv2022

Invasion and Interaction Determine Population Composition in an Open Evolving System

It is well-known that interactions between species determine the population composition in an ecosystem. Conventional studies have focused on fixed population structures to reveal how interactions shape population compositions. However, interaction structures are not fixed, but change over time due to invasions. Thus, invasion and interaction play an important role in shaping communities. Despite its importance, however, the interplay between invasion and interaction has not been well explored. Here, we investigate how invasion affects the population composition with interactions in open evolving systems considering generalized Lotka-Volterra-type dynamics. Our results show that the system has two distinct regimes. One is characterized by low diversity with abrupt changes of dominant species in time, appearing when the interaction between species is strong and invasion slowly occurs. On the other hand, frequent invasions can induce higher diversity with slow changes in abundances despite strong interactions. It is because invasion happens before the system reaches its equilibrium, which drags the system from its equilibrium all the time. All species have similar abundances in this regime, which implies that fast invasion induces regime shift. Therefore, whether invasion or interaction dominates determines the population composition.

q-bio.PE↗

arXiv2020

Motif Dynamics in Signed Directional Complex Networks

Complex networks evolve and vary their structure as time goes by. In particular, the links in those networks have both a sign and a directionality. To understand their structural principles, we measure the network motifs, which are patterns that appear much more than one would expect in randomized networks, considering both link properties. We propose motif dynamics, which is a study to investigate the change in the number of motifs, and applied the motif dynamics to an open evolving network model and empirical data. We confirm that a non-cyclic motif has a greater correlation with the system size than a cyclic structural motif. Furthermore, the motif dynamics can give us insight into the friendship between freshmen in a university.

physics.soc-ph↗