arXiv subjects

Gi-Hwan Shin

Publications and source records attributed to Gi-Hwan Shin.

At least 19 recordsLinked to original sources

arXiv2025

A Quantitative Framework for Assessing Sleep Quality from EEG Time Series in Complex Dynamic Systems

Modern lifestyles contribute to insufficient sleep, impairing cognitive function and weakening the immune system. Sleep quality (SQ) is vital for physiological and mental health, making its understanding and accurate assessment critical. However, its multifaceted nature, shaped by neurological and environmental factors, makes precise quantification challenging. Here, we address this challenge by utilizing electroencephalography (EEG) for phase-amplitude coupling (PAC) analysis to elucidate the neurological basis of SQ, examining both states of sleep and wakefulness, including resting state (RS) and working memory. Our results revealed distinct patterns in beta power and delta connectivity in sleep and RS, together with the reaction time of working memory. A notable finding was the pronounced delta-beta PAC, a feature markedly stronger in individuals with good SQ. We further observed that SQ was positively correlated with increased delta-beta PAC. Leveraging these insights, we applied machine learning models to classify SQ at an individual level, demonstrating that the delta-beta PAC outperformed other EEG characteristics. These findings establish delta-beta PAC as a robust electrophysiological marker to quantify SQ and elucidate its neurological determinants.

cs.HC↗

arXiv2025

Personalized targeted memory reactivation enhances consolidation of challenging memories via slow wave and spindle dynamics

Sleep is crucial for memory consolidation, underpinning effective learning. Targeted memory reactivation (TMR) can strengthen neural representations by re-engaging learning circuits during sleep. However, TMR protocols overlook individual differences in learning capacity and memory trace strength, limiting efficacy for difficult-to-recall memories. Here, we present a personalized TMR protocol that adjusts stimulation frequency based on individual retrieval performance and task difficulty during a word-pair memory task. In an experiment comparing personalized TMR, TMR, and control groups, the personalized protocol significantly reduced memory decay and improved error correction under challenging recall. Electroencephalogram (EEG) analyses revealed enhanced synchronization of slow waves and spindles, with a significant positive correlation between behavioral and EEG features for challenging memories. Multivariate classification identified distinct neural signatures linked to the personalized approach, highlighting its ability to target memory-specific circuits. These findings provide novel insights into sleep-dependent memory consolidation and support personalized TMR interventions to optimize learning outcomes.

cs.HC↗

arXiv2025

Neurophysiological Characteristics of Adaptive Reasoning for Creative Problem-Solving Strategy

Adaptive reasoning enables humans to flexibly adjust inference strategies when environmental rules or contexts change, yet its underlying neural dynamics remain unclear. This study investigated the neurophysiological mechanisms of adaptive reasoning using a card-sorting paradigm combined with electroencephalography and compared human performance with that of a multimodal large language model. Stimulus- and feedback-locked analyses revealed coordinated delta-theta-alpha dynamics: early delta-theta activity reflected exploratory monitoring and rule inference, whereas occipital alpha engagement indicated confirmatory stabilization of attention after successful rule identification. In contrast, the multimodal large language model exhibited only short-term feedback-driven adjustments without hierarchical rule abstraction or genuine adaptive reasoning. These findings identify the neural signatures of human adaptive reasoning and highlight the need for brain-inspired artificial intelligence that incorporates oscillatory feedback coordination for true context-sensitive adaptation.

cs.AI↗

arXiv2025

Toward Practical BCI: A Real-time Wireless Imagined Speech EEG Decoding System

Brain-computer interface (BCI) research, while promising, has largely been confined to static and fixed environments, limiting real-world applicability. To move towards practical BCI, we introduce a real-time wireless imagined speech electroencephalogram (EEG) decoding system designed for flexibility and everyday use. Our framework focuses on practicality, demonstrating extensibility beyond wired EEG devices to portable, wireless hardware. A user identification module recognizes the operator and provides a personalized, user-specific service. To achieve seamless, real-time operation, we utilize the lab streaming layer to manage the continuous streaming of live EEG signals to the personalized decoder. This end-to-end pipeline enables a functional real-time application capable of classifying user commands from imagined speech EEG signals, achieving an overall 4-class accuracy of 62.00 % on a wired device and 46.67 % on a portable wireless headset. This paper demonstrates a significant step towards truly practical and accessible BCI technology, establishing a clear direction for future research in robust, practical, and personalized neural interfaces.

cs.AI↗

arXiv2025

Consciousness-ECG Transformer for Conscious State Estimation System with Real-Time Monitoring

Conscious state estimation is important in various medical settings, including sleep staging and anesthesia management, to ensure patient safety and optimize health outcomes. Traditional methods predominantly utilize electroencephalography (EEG), which faces challenges such as high sensitivity to noise and the requirement for controlled environments. In this study, we propose the consciousness-ECG transformer that leverages electrocardiography (ECG) signals for non-invasive and reliable conscious state estimation. Our approach employs a transformer with decoupled query attention to effectively capture heart rate variability features that distinguish between conscious and unconscious states. We implemented the conscious state estimation system with real-time monitoring and validated our system on datasets involving sleep staging and anesthesia level monitoring during surgeries. Experimental results demonstrate that our model outperforms baseline models, achieving accuracies of 0.877 on sleep staging and 0.880 on anesthesia level monitoring. Moreover, our model achieves the highest area under curve values of 0.786 and 0.895 on sleep staging and anesthesia level monitoring, respectively. The proposed system offers a practical and robust alternative to EEG-based methods, particularly suited for dynamic clinical environments. Our results highlight the potential of ECG-based consciousness monitoring to enhance patient safety and advance our understanding of conscious states.

eess.SP↗

arXiv2024

Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms

In this paper, we propose a conceptual framework for personalized brain-computer interface (BCI) applications, which can offer an enhanced user experience by customizing services to individual preferences and needs, based on endogenous electroencephalography (EEG) paradigms including motor imagery (MI), speech imagery (SI), and visual imagery. The framework includes two essential components: user identification and intention classification, which enable personalized services by identifying individual users and recognizing their intended actions through EEG signals. We validate the feasibility of our framework using a private EEG dataset collected from eight subjects, employing the ShallowConvNet architecture to decode EEG features. The experimental results demonstrate that user identification achieved an average classification accuracy of 0.995, while intention classification achieved 0.47 accuracy across all paradigms, with MI demonstrating the best performance. These findings indicate that EEG signals can effectively support personalized BCI applications, offering robust identification and reliable intention decoding, especially for MI and SI.

cs.HC↗

arXiv2023

Relationship Between Mood, Sleepiness, and EEG Functional Connectivity by 40 Hz Monaural Beats

The monaural beat is known that it can modulate brain and personal states. However, which changes in brain waves are related to changes in state is still unclear. Therefore, we aimed to investigate the effects of monaural beats and find the relationship between them. Ten participants took part in five separate random sessions, which included a baseline session and four sessions with monaural beats stimulation: one audible session and three inaudible sessions. Electroencephalogram (EEG) were recorded and participants completed pre- and post-stimulation questionnaires assessing mood and sleepiness. As a result, audible session led to increased arousal and positive mood compared to other conditions. From the neurophysiological analysis, statistical differences in frontal-central, central-central, and central-parietal connectivity were observed only in the audible session. Furthermore, a significant correlation was identified between sleepiness and EEG power in the temporal and occipital regions. These results suggested a more detailed correlation for stimulation to change its personal state. These findings have implications for applications in areas such as cognitive enhancement, mood regulation, and sleep management.

q-bio.NC↗

arXiv2023

Impact of Nap on Performance in Different Working Memory Tasks Using EEG

Electroencephalography (EEG) has been widely used to study the relationship between naps and working memory, yet the effects of naps on distinct working memory tasks remain unclear. Here, participants performed word-pair and visuospatial working memory tasks pre- and post-nap sessions. We found marked differences in accuracy and reaction time between tasks performed pre- and post-nap. In order to identify the impact of naps on performance in each working memory task, we employed clustering to classify participants as high- or low-performers. Analysis of sleep architecture revealed significant variations in sleep onset latency and rapid eye movement (REM) proportion. In addition, the two groups exhibited prominent differences, especially in the delta power of the Non-REM 3 stage linked to memory. Our results emphasize the interplay between nap-related neural activity and working memory, underlining specific EEG markers associated with cognitive performance.

q-bio.NC↗

arXiv2023

Neurophysiological Response Based on Auditory Sense for Brain Modulation Using Monaural Beat

Brain modulation is a modification process of brain activity through external stimulations. However, which condition can induce the activation is still unclear. Therefore, we aimed to identify brain activation conditions using 40 Hz monaural beat (MB). Under this stimulation, auditory sense status which is determined by frequency and power range is the condition to consider. Hence, we designed five sessions to compare; no stimulation, audible (AB), inaudible in frequency, inaudible in power, and inaudible in frequency and power. Ten healthy participants underwent each stimulation session for ten minutes with electroencephalogram (EEG) recording. For analysis, we calculated the power spectral density (PSD) of EEG for each session and compared them in frequency, time, and five brain regions. As a result, we observed the prominent power peak at 40 Hz in only AB. The induced EEG amplitude increase started at one minute and increased until the end of the session. These results of AB had significant differences in frontal, central, temporal, parietal, and occipital regions compared to other stimulations. From the statistical analysis, the PSD of the right temporal region was significantly higher than the left. We figure out the role that the auditory sense is important to lead brain activation. These findings help to understand the neurophysiological principle and effects of auditory stimulation.

q-bio.NC↗

arXiv2023

Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography

Polysomnography (PSG) is an indispensable diagnostic tool in sleep medicine, essential for identifying various sleep disorders. By capturing physiological signals, including EEG, EOG, EMG, and cardiorespiratory metrics, PSG presents a patient's sleep architecture. However, its dependency on complex equipment and expertise confines its use to specialized clinical settings. Addressing these limitations, our study aims to perform PSG by developing a system that requires only a single EEG measurement. We propose a novel system capable of reconstructing multi-signal PSG from a single-channel EEG based on a masked autoencoder. The masked autoencoder was trained and evaluated using the Sleep-EDF-20 dataset, with mean squared error as the metric for assessing the similarity between original and reconstructed signals. The model demonstrated proficiency in reconstructing multi-signal data. Our results present promise for the development of more accessible and long-term sleep monitoring systems. This suggests the expansion of PSG's applicability, enabling its use beyond the confines of clinics.

cs.LG↗

arXiv2022

Changes in Power and Information Flow in Resting-state EEG by Working Memory Process

Many studies have analyzed working memory (WM) from electroencephalogram (EEG). However, little is known about changes in the brain neurodynamics among resting-state (RS) according to the WM process. Here, we identified frequency-specific power and information flow patterns among three RS EEG before and after WM encoding and WM retrieval. Our results demonstrated the difference in power and information flow among RS EEG in delta (1-3.5 Hz), alpha (8-13.5 Hz), and beta (14-29.5 Hz) bands. In particular, there was a marked increase in the alpha band after WM retrieval. In addition, we calculated the association between significant characteristics of RS EEG and WM performance, and interestingly, correlations were found only in the alpha band. These results suggest that RS EEG according to the WM process has a significant impact on the variability and WM performance of brain mechanisms in relation to cognitive function.

q-bio.NC↗

arXiv2022

Development of Personalized Sleep Induction System based on Mental States

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7 percent and stopped auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants.

cs.HC↗

arXiv2022

Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling

In this paper, we propose a Siamese sleep transformer (SST) that effectively extracts features from single-channel raw electroencephalogram signals for robust sleep stage scoring. Despite the significant advances in sleep stage scoring in the last few years, most of them mainly focused on the increment of model performance. However, other problems still exist: the bias of labels in datasets and the instability of model performance by repetitive training. To alleviate these problems, we propose the SST, a novel sleep stage scoring model with a selective batch sampling strategy and self-knowledge distillation. To evaluate how robust the model was to the bias of labels, we used different datasets for training and testing: the sleep heart health study and the Sleep-EDF datasets. In this condition, the SST showed competitive performance in sleep stage scoring. In addition, we demonstrated the effectiveness of the selective batch sampling strategy with a reduction of the standard deviation of performance by repetitive training. These results could show that SST extracted effective learning features against the bias of labels in datasets, and the selective batch sampling strategy worked for the model robustness in training.

eess.SP↗

arXiv2021

Possibility of Sleep Induction using Auditory Stimulation based on Mental States

Sleep has a significant role to maintain our health. However, people have struggled with sleep induction because of noise, emotion, and complicated thoughts. We hypothesized that there was more effective auditory stimulation to induce sleep based on their mental states. We investigated five auditory stimulation: sham, repetitive beep, binaural beat, white noise, and rainy sounds. The Pittsburgh sleep quality index was performed to divide subjects into good and poor sleep groups. To verify the subject's mental states between initiation of sessions, a psychomotor vigilance task and Stanford sleepiness scale (SSS) were performed before auditory stimulation. After auditory stimulation, we asked subjects to report their sleep experience during auditory stimulation. We also calculated alpha dominant duration that was the period that represents the wake period during stimulation. We showed that there were no differences in reaction time and SSS between sessions. It indicated sleep experience is not related to the timeline. The good sleep group fell asleep more frequently than the poor sleep group when they hear white noise and rainy sounds. Moreover, when subjects failed to fall asleep during sham, most subjects fell asleep during rainy sound (Cohen's kappa: -0.588). These results help people to select suitable auditory stimulation to induce sleep based on their mental states.

cs.HC↗

arXiv2021

Differential EEG Characteristics during Working Memory Encoding and Re-encoding

Many studies have discussed the difference in brain activity related to encoding and retrieval of working memory (WM) tasks. However, it remains unclear if there is a change in brain activation associated with re-encoding. The main objective of this study was to compare different brain states (rest, encoding, and re-encoding) during the WM task. We recorded brain activity from thirty-seven participants using an electroencephalogram and calculated power spectral density (PSD) and phase-locking value (PLV) for different frequencies. In addition, the difference in phase-amplitude coupling (PAC) between encoding and re-encoding was investigated. Our results showed that alpha PSD decreased as the learning progressed, and theta PLV, beta PLV, and gamma PLV showed differences between brain regions. Also, there was a statistically significant difference in PAC. These findings suggest the possibility of improving the efficiency of learning during re-encoding by understanding the differences in neural correlation related to learning.

q-bio.NC↗

arXiv2021

Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running

We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.

cs.HC↗

arXiv2020

Automatic Micro-sleep Detection under Car-driving Simulation Environment using Night-sleep EEG

A micro-sleep is a short sleep that lasts from 1 to 30 secs. Its detection during driving is crucial to prevent accidents that could claim a lot of people's lives. Electroencephalogram (EEG) is suitable to detect micro-sleep because EEG was associated with consciousness and sleep. Deep learning showed great performance in recognizing brain states, but sufficient data should be needed. However, collecting micro-sleep data during driving is inefficient and has a high risk of obtaining poor data quality due to noisy driving situations. Night-sleep data at home is easier to collect than micro-sleep data during driving. Therefore, we proposed a deep learning approach using night-sleep EEG to improve the performance of micro-sleep detection. We pre-trained the U-Net to classify the 5-class sleep stages using night-sleep EEG and used the sleep stages estimated by the U-Net to detect micro-sleep during driving. This improved micro-sleep detection performance by about 30\% compared to the traditional approach. Our approach was based on the hypothesis that micro-sleep corresponds to the early stage of non-rapid eye movement (NREM) sleep. We analyzed EEG distribution during night-sleep and micro-sleep and found that micro-sleep has a similar distribution to NREM sleep. Our results provide the possibility of similarity between micro-sleep and the early stage of NREM sleep and help prevent micro-sleep during driving.

cs.LG↗

arXiv2020

Predicting the Transition from Short-term to Long-term Memory based on Deep Neural Network

Memory is an essential element in people's daily life based on experience. So far, many studies have analyzed electroencephalogram (EEG) signals at encoding to predict later remembered items, but few studies have predicted long-term memory only with EEG signals of successful short-term memory. Therefore, we aim to predict long-term memory using deep neural networks. In specific, the spectral power of the EEG signals of remembered items in short-term memory was calculated and inputted to the multilayer perceptron (MLP) and convolutional neural network (CNN) classifiers to predict long-term memory. Seventeen participants performed visuo-spatial memory task consisting of picture and location memory in the order of encoding, immediate retrieval (short-term memory), and delayed retrieval (long-term memory). We applied leave-one-subject-out cross-validation to evaluate the predictive models. As a result, the picture memory showed the highest kappa-value of 0.19 on CNN, and location memory showed the highest kappa-value of 0.32 in MLP. These results showed that long-term memory can be predicted with measured EEG signals during short-term memory, which improves learning efficiency and helps people with memory and cognitive impairments.

cs.NE↗