TY - GEN

T1 - Modeling the Dynamics of Changes in EEG Fractal Dimension during Problem Solving

AU - Горбунов, Иван Анатольевич

AU - Морозова, Светлана Васильевна

AU - Чуканов, Андрей Владимирович

AU - Соснина, Юлия Сергеевна

N1 - Conference code: 29

PY - 2025/9/11

Y1 - 2025/9/11

N2 - Understanding the relationship between energy cost, cognitive activity, and performance is a key problem in psychophysiology. Prior studies have shown that solving cognitive tasks is accompanied by changes in the fractal dimension of EEG signals. In the present study, EEG was recorded from 68 participants solving a task in which participants matched graphs and tables. Participants identified which of three tables matched the numerical data presented in a graph. EEG signals were analyzed using the Higuchi fractal dimension algorithm. The results showed a short-term increase followed by a gradual decrease in EEG fractal dimension (F(4, 7656) = 36.594, p < 0.0001). We modeled this effect in the dynamics of neural network training. The first model was trained to classify 2 input values in the range from -π/2 to π/2. The target function was sinusoidal with further classification into two states: 0 or 1. At each training step, we summed the activations across all network neurons and computed the fractal dimension, which briefly increased and then gradually declined. The second model, based on ResNet18 and modified to solve the task of matching graphs and tables, showed a monotonic decrease in fractal dimension.

AB - Understanding the relationship between energy cost, cognitive activity, and performance is a key problem in psychophysiology. Prior studies have shown that solving cognitive tasks is accompanied by changes in the fractal dimension of EEG signals. In the present study, EEG was recorded from 68 participants solving a task in which participants matched graphs and tables. Participants identified which of three tables matched the numerical data presented in a graph. EEG signals were analyzed using the Higuchi fractal dimension algorithm. The results showed a short-term increase followed by a gradual decrease in EEG fractal dimension (F(4, 7656) = 36.594, p < 0.0001). We modeled this effect in the dynamics of neural network training. The first model was trained to classify 2 input values in the range from -π/2 to π/2. The target function was sinusoidal with further classification into two states: 0 or 1. At each training step, we summed the activations across all network neurons and computed the fractal dimension, which briefly increased and then gradually declined. The second model, based on ResNet18 and modified to solve the task of matching graphs and tables, showed a monotonic decrease in fractal dimension.

KW - EEG

KW - Fractal Dimension

KW - Neural Network

KW - Entropy

KW - Higuchi Algorithm

UR - https://www.iiis.org/DOI2025/SA852VC/

UR - https://www.mendeley.com/catalogue/d2ceb646-d95d-3fab-8b07-cf7d2267cceb/

U2 - 10.54808/wmsci2025.01.487

DO - 10.54808/wmsci2025.01.487

M3 - статья в сборнике материалов конференции

SN - 978-1-950492-85-5

T3 - Proceedings

SP - 487

EP - 492

BT - Proceedings of the 29th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2025

PB - International Institute of Informatics and Cybernetics

T2 - The 29th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2025<br/>

Y2 - 9 September 2025 through 12 September 2025

ER -