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23rd International Conference on Cyberworlds, CW 2024

作者： Lee, Hyung-Tak Bak, Keungyonh Chun, Sungwoo Hwang, Han-Jeong Korea University Dept. Electronics and Information Engineering Sejong Korea Republic of Korea University Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Sejong Korea Republic of

ISBN: (纸本)9798331527174

To enhance the sense of reality in the VR environment, it is essential to incorporate not only visual and hearing but also tactile feedback. Previous studies related to tactile sensation have only focused on object classification to identify the object in contact with the tactile sensor. When people encounter a new object, they can use tactile perception information, such as roughness, to understand the object's attributes and functions. Therefore, for tactile research to be applicable in real-life scenarios, tactile perception classification is essential not only for classifying an object but also for recognizing its attributes and functions. This study aims to investigate the possibility of predicting roughness, one of the important human tactile perceptions, using deep learning models. We created 63 tactile samples, measured the vibration data of each sample using an acceleration sensor, and investigated how humans perceive the roughness of the tactile samples with five different levels. Object classification was performed using five deep learning models, among which the Shallow ConvNet achieved the highest performance, confirming its suitability as the most appropriate model for analyzing vibration data. Using the Shallow ConvNet, we attained a high accuracy of 98.6 ±5.6% for the identification of five levels of roughness, demonstrating that different levels of tactile perception information can be discriminated with high performance. © 2024 IEEE.

关键词： Deep learning Human tactile perception Roughness Vibration data Virtual reality

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Development of EEG-Based Optimal Biomarkers for Decoding Fatigue during Motor Imagery Tasks 13

Development of EEG-Based Optimal Biomarkers for Decoding Fat...

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13th International Winter Conference on Brain-Computer Interface, BCI 2025

作者： Lee, Jun-Seok Lee, Seung-Won Kim, Sang-Rok Hwang, Han-Jeong Dept. Electronics and Information Engineering Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of

ISBN: (纸本)9798331521929

This study aimed to identify electroencephalography (EEG)-based neurophysiological biomarkers for monitoring mental fatigue during motor imagery (MI) tasks. We used EEG data recorded from 29 healthy adults during MI tasks. For the preprocessing of the EEG data, we sequentially performed down-sampling into 200 Hz, independent component analysis (ICA), band-pass filtering between 0.5 and 50 Hz, and common average reference (CAR). The preprocessed EEG data were analyzed using a fast Fourier transform (FFT) to calculate spectral power in the δ (0.5–3 Hz), θ (4–7 Hz), α (8–13 Hz), and β (13–30 Hz) frequency bands. Subsequently, seventeen fatigue-related metrics were calculated using individual spectral powers and their combinations. These fatigue-related metrics were averaged for the frontal lobe (F3 and F4), central lobe (FCC3h and FCC4h), parietal lobe (P3 and P4), and occipital lobe (POO1 and POO2). The statistical significance of the average fatigue metrics was evaluated through Pearson correlation analysis with the sequence of trials. As a result, we observed strong positive correlations (r > 0.9, p © 2025 IEEE.

关键词： Fast Fourier transforms

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A review of hybrid EEG-based multimodal human–computer interfaces using deep learning: applications, advances, and challenges

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Biomedical Engineering Letters 2025年 1-32页

作者： Lee, Hyung-Tak Shim, Miseon Liu, Xianghong Cheon, Hye-Ran Kim, Sang-Gyu Han, Chang-Hee Hwang, Han-Jeong Department of Electronics and Information Engineering Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Department of Artificial Intelligence Tech University of Korea Siheung Korea Republic of Department of Computer Information Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Korea Republic of

Human–computer interaction (HCI) focuses on designing efficient and intuitive interactions between humans and computer systems. Recent advancements have utilized multimodal approaches, such as electroencephalography (EEG)-based systems combined with other biosignals, along with deep learning to enhance performance and reliability. However, no systematic review has consolidated findings on EEG-based multimodal HCI systems. This review examined 124 studies published from 2016 to 2024, retrieved from the Web of Science database, focusing on hybrid EEG-based multimodal HCI systems employing deep learning. The keywords used for evaluation were as follows: ‘Deep Learning’ AND ‘EEG’ AND (‘fNIRS’ OR ‘NIRS’ OR ‘MEG’ OR ‘fMRI’ OR ‘EOG’ OR ‘EMG’ OR ‘ECG’ OR ‘PPG’ OR ‘GSR’). Main topics explored are: (1) types of biosignals used with EEG, (2) neural network architectures, (3) fusion strategies, (4) system performance, and (5) target applications. Frequently paired signals, such as EOG, EMG, and fNIRS, effectively complement EEG by addressing its limitations. Convolutional neural networks are extensively employed for spatio-temporal-spectral feature extraction, with early and intermediate fusion strategies being the most commonly used. Applications, such as sleep stage classification, emotion recognition, and mental state decoding, have shown notable performance improvements. Despite these advancements, challenges remain, including the lack of real-time online systems, difficulties in signal synchronization, limited data availability, and insufficient explainable AI (XAI) methods to interpret signal interactions. Emerging solutions, such as portable systems, lightweight deep learning models, and data augmentation techniques, offer promising pathways to address these issues. This review highlights the potential of EEG-based multimodal HCI systems and emphasizes the need for advancements in real-time interaction, fusion algorithms, and XAI to enhance their adaptability, interpreta

关键词： Convolutional neural networks

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Effect of White Noise on Working Memory Using Event-Related Potentials

Effect of White Noise on Working Memory Using Event-Related ...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Seung-Won Lee Jun-Seok Lee Han-Jeong Hwang Department of Electronics and Information Engineering Korea University Sejong Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Korea

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

We investigated the effect of white noise on working memory performance using electroencephalography (EEG). Six healthy subjects participated in this study, and each subject performed a 2-back task under white noise and no sound conditions. After a series of EEG preprocessing, the amplitude of event-related potential (ERP) was calculated. The results indicated an increase in average accuracy and mean P300 amplitude during the white noise condition compared to without it, suggesting that white noise might enhance working memory performance. However, due to the limited number of participants, statistical power was not attained. In our future studies, we will perform additional experiments with more subjects.

关键词： Electric potential Accuracy Asia Information processing White noise Electroencephalography

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VR-Based Implicit Authentication Using Electromyogram and Inertial Measurement Unit 23

VR-Based Implicit Authentication Using Electromyogram and In...

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23rd International Conference on Cyberworlds, CW 2024

作者： Yu, Da-Young Lee, Hyung-Tak Kim, Youngsam Roh, Jong-Hyuk Kim, Soohyung Ku, Jeonghun Hwang, Han-Jeong Korea University Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Dept. Electronics and Information Engineering Sejong Korea Republic of Korea University Dept. Electronics and Information Engineering Sejong Korea Republic of Daejeon Korea Republic of Keimyung University College of Medicine Department of Biomedical Engineering Daegu Korea Republic of

ISBN: (纸本)9798331527174

Biometrics based on electromyogram (EMG) and inertial measurement unit (IMU) signals has received significant attention due to its resistance to forgery and tampering, as well as its relatively higher convenience and security compared to other biometric modalities. However, most EMG and IMU signal-based authentication methods require explicit authentication using unnatural gestures, which is not userfriendly. To address this, we collected EMG and IMU data during virtual reality (VR) rhythm game play, which does not require explicit gestures, and performed implicit authentication. Using a linear discriminant analysis (LDA) classifier, we achieved classification accuracies of 91.71% for human identification and 87.91% for human authentication. The result demonstrated the feasibility of a seamless and implicit authentication in VR environments. © 2024 IEEE.

关键词： Electromyography

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Effect of Dynamic Binaural Beats on Concentration Enhancement

Effect of Dynamic Binaural Beats on Concentration Enhancemen...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Jun-Seok Lee Yun-Sung Lee Han-Jeong Hwang Dept. of Electronics and Information Engineering Korea University Sejong Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Korea

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

The objective of this study was to develop an auditory stimulation method that can improve concentration and to investigate its neurophysiological effects using electrocardiography (ECG). The auditory stimulus was developed by mixing the sound of white noise with dynamic binaural beats (DBB) that featured time-varying carrier frequencies. The experimental paradigm involved conducting an association memory (AM) task and a mental arithmetic (MA) task to assess cognitive performance. In the behavioral results, no significant differences were confirmed between the two experimental conditions (sound vs. no sound) for both cognitive tasks. In the AM task, no significant differences in HRV change between the baseline and task were observed between the two conditions. However, during the MA task, there was a significant decrease in HRV when participants were exposed to the auditory stimulation compared to when it was not.

关键词： Time-frequency analysis Rain Asia Information processing Electrocardiography White noise Heart rate variability Arithmetic

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Development of EEG-Based Optimal Biomarkers for Decoding Fatigue during Motor Imagery Tasks

Development of EEG-Based Optimal Biomarkers for Decoding Fat...

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International Winter Workshop on Brain-Computer Interface (BCI)

作者： Jun-Seok Lee Seung-Won Lee Sang-Rok Kim Han-Jeong Hwang Dept. Electronics and Information Engineering Korea University Sejong Republic of Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Republic of Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

This study aimed to identify electroencephalography (EEG)-based neurophysiological biomarkers for monitoring mental fatigue during motor imagery (MI) tasks. We used EEG data recorded from 29 healthy adults during MI tasks. For the preprocessing of the EEG data, we sequentially performed down-sampling into 200 Hz, independent component analysis (ICA), band-pass filtering between 0.5 and 50 Hz, and common average reference (CAR). The preprocessed EEG data were analyzed using a fast Fourier transform (FFT) to calculate spectral power in the δ (0.5-3 Hz), θ (4–7 Hz), $a$ (8–13 Hz), and β (13–30 Hz) frequency bands. Subsequently, seventeen fatigue-related metrics were calculated using individual spectral powers and their combinations. These fatigue-related metrics were averaged for the frontal lobe (F3 and F4), central lobe (FCC3h and FCC4h), parietal lobe (P3 and P4), and occipital lobe (POO1 and POO2). The statistical significance of the average fatigue metrics was evaluated through Pearson correlation analysis with the sequence of trials. As a result, we observed strong positive correlations (r < 0.9, p < 0.001) between beta power in the frontal and central regions and the sequence of trials. These findings suggest that central and frontal beta activity can serve as reliable biomarkers for estimating fatigue during MI tasks, supporting their potential for real-time fatigue monitoring.

关键词： Measurement Correlation Fast Fourier transforms Biomarkers Fatigue Motors Electroencephalography Decoding Reliability Monitoring

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Classification of Human Tactile Perception Using Vibration Data

Classification of Human Tactile Perception Using Vibration D...

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International Conference on Cyberworlds

作者： Hyung-Tak Lee Keungyonh Bak Sungwoo Chun Han-Jeong Hwang Dept. Electronics and Information Engineering Korea University Sejong Republic of Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Republic of Korea

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

To enhance the sense of reality in the VR environment, it is essential to incorporate not only visual and hearing but also tactile feedback. Previous studies related to tactile sensation have only focused on object classification to identify the object in contact with the tactile sensor. When people encounter a new object, they can use tactile perception information, such as roughness, to understand the object’s attributes and functions. Therefore, for tactile research to be applicable in real-life scenarios, tactile perception classification is essential not only for classifying an object but also for recognizing its attributes and functions. This study aims to investigate the possibility of predicting roughness, one of the important human tactile perceptions, using deep learning models. We created 63 tactile samples, measured the vibration data of each sample using an acceleration sensor, and investigated how humans perceive the roughness of the tactile samples with five different levels. Object classification was performed using five deep learning models, among which the Shallow ConvNet achieved the highest performance, confirming its suitability as the most appropriate model for analyzing vibration data. Using the Shallow ConvNet, we attained a high accuracy of $\mathbf{9 8. 6} \pm \mathbf{5. 6 \%}$ for the identification of five levels of roughness, demonstrating that different levels of tactile perception information can be discriminated with high performance.

关键词： Vibrations Deep learning Solid modeling Analytical models Visualization Tactile sensors Virtual reality Vibration measurement Predictive models Data models

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A Deep Learning Model of Amyloid-β Diffusion Simulation

A Deep Learning Model of Amyloid-β Diffusion Simulation

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Jeong, ByeongChang Kim, Daegyeom Jeong, Hyun-Ghang Han, Cheol E. Korea University Alzheimer's Disease Neuroimaging Initiative Dept. of Electronic and Information Engineering Sejong Korea Republic of Korea University Alzheimer's Disease Neuroimaging Initiative Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Sejong Korea Republic of Korea University College of Medicine Alzheimer's Disease Neuroimaging Initiative Dept. of Psychiatry Seoul Korea Republic of

ISBN: (纸本)9798350371888

Alzheimer's disease (AD) is a severe neurodegenerative disease characterized by ongoing brain tissue decline. Since amyloid-β accumulation is supposed as a primary cause of AD progression, mathematical models have been recently employed to simulate amyloid-β diffusion in our brain. These models require parameter estimation through observed data and an iterative process to update parameters, although increased complexity can hinder their optimization. To overcome these difficulties, we converted the mathematical model of amyloid-β diffusion into a deep learning model, combining multi-layer perceptron (MLP) and graph convolutional neural network (GCN). The proposed model well predicted the change of accumulation level of amyloid-β with a high accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils

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Computers in Biology and Medicine 2025年 184卷 109410-109410页

作者： Park, Jaehee Cheong, Da Yeon Lee, Gyudo Han, Cheol E. Department of Electronics and Information Engineering Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicated in various diseases, such as Alzheimer's disease, Parkinson's disease, type II diabetes, and various forms of amyloidosis. Atomic force microscopy (AFM) stands as the most representative method for studying amyloid fibril morphology. However, obstacles in AFM images, including noise, salt, and amorphous aggregates, often impede accurate sample quantification. In this study, we developed denoising software employing a U-Net deep learning architecture to address this issue. The software efficiently eliminated various impediments that interfere with fibril analysis in noisy AFM images, thereby facilitating precise quantification of amyloid fibrils. We also developed automated fibril analysis technologies using the denoised AFM images, leading to quicker, more precise, and more objective assessments of fibril morphology. Furthermore, we presented a method for fibril stiffness extraction from a modulus image through mask creation based on a denoised height image. Our approach secures time efficiency and precision in analyzing amyloid morphology, and we believe it will significantly advance the currently stagnant research on amyloid-related diseases. © 2024 The Authors

关键词： Neurodegenerative diseases

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