Chee Meng Benjamin Ho
Research Subjects
Chee Meng Benjamin Ho has a total of 15 co-authors. Here are the top co-authors by number of shared publications:
- Se Jin Park
Korea Research Institute of Standards and Science
3 shared publications - Jaehak Yu
Department of KSB Convergence Research, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
2 shared publications - Chang Ho Kim
UNITECH CO., Ltd., Seoul 08289, Korea
1 shared publication - Cheol-Sig Pyo
Department of KSB Convergence Research, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
1 shared publication Hana Jeong
AI Research Group, Sewon Intelligence, Seoul, Korea
1 shared publication- Hansung Lee
Department of Computer Education, Gyeongkuk National University (GKNU), Andong, Gyeongsangbuk-do, Republic of Korea
1 shared publication
Latest Publications
- 29 November 2022
- Published by Springer Nature in Applied Psychophysiology and Biofeedback
- Vol. 48 (2) , 207-216
Effects of Audio Brain Entrainment on Korean People with Mild Insomnia
Sleep health has become an important healthy lifestyle. Research has shown that almost one-fifth of the Korean adult population does not have sufficient sleep. The lack of sleep is associated with significant medical, psychological, social, and economic issues. People are not only yearning for sufficient sleep but the quality of sleep as well. Usually, the obvious choice will be the use of pharmaceuticals however, these often have various side effects, and the lasting use of these medications could become a concern. Therefore, new non-drug alternatives are sought after. Audio brain entrainment is a procedure that modules neural activities by synchronizing brainwave frequency with pulse tones. By producing frequency tones for the deep sleep stage, it promotes a good night’s sleep. In this paper, we developed a pillow integrated with the audio speakers that produce alpha and theta beats that should help improve sleep. Sleep polysomnography was performed on 10 people to compare the effects of the audio stimulus. Initial results showed a positive effect on sleep onset latency, indicating that sleep induction happened. This noninvasive stimulation technique can be a promising candidate for wearable bioelectronics medicine and further neuroscience research.
- 29 November 2022
- Published by Springer Nature in Applied Psychophysiology and Biofeedback
- Vol. 48 (2) , 207-216
Effects of Audio Brain Entrainment on Korean People with Mild Insomnia
Sleep health has become an important healthy lifestyle. Research has shown that almost one-fifth of the Korean adult population does not have sufficient sleep. The lack of sleep is associated with significant medical, psychological, social, and economic issues. People are not only yearning for sufficient sleep but the quality of sleep as well. Usually, the obvious choice will be the use of pharmaceuticals however, these often have various side effects, and the lasting use of these medications could become a concern. Therefore, new non-drug alternatives are sought after. Audio brain entrainment is a procedure that modules neural activities by synchronizing brainwave frequency with pulse tones. By producing frequency tones for the deep sleep stage, it promotes a good night’s sleep. In this paper, we developed a pillow integrated with the audio speakers that produce alpha and theta beats that should help improve sleep. Sleep polysomnography was performed on 10 people to compare the effects of the audio stimulus. Initial results showed a positive effect on sleep onset latency, indicating that sleep induction happened. This noninvasive stimulation technique can be a promising candidate for wearable bioelectronics medicine and further neuroscience research.
- 14 January 2022
- Published by Springer Nature in The Journal of Supercomputing
- Vol. 78 (6) , 8867-8889
AI-based stroke prediction system using body motion biosignals during walking
- 14 January 2022
- Published by Springer Nature in The Journal of Supercomputing
- Vol. 78 (6) , 8867-8889
AI-based stroke prediction system using body motion biosignals during walking
- Open Access
Quantifying Physiological Biomarkers of a Microwave Brain Stimulation Device
Physiological signals are immediate and sensitive to neural and cardiovascular change resulting from brain stimulation, and are considered as a quantifying tool with which to evaluate the association between brain stimulation and cognitive performance. Brain stimulation outside a highly equipped, clinical setting requires the use of a low-cost, ambulatory miniature system. The purpose of this double-blind, randomized, sham-controlled study is to quantify the physiological biomarkers of the neural and cardiovascular systems induced by a microwave brain stimulation (MBS) device. We investigated the effect of an active MBS and a sham device on the cardiovascular and neurological responses of ten volunteers (mean age 26.33 years, 70% male). Electroencephalography (EEG) and electrocardiography (ECG) were recorded in the initial resting-state, intermediate state, and the final state at half-hour intervals using a portable sensing device. During the experiment, the participants were engaged in a cognitive workload. In the active MBS group, the power of high-alpha, high-beta, and low-beta bands in the EEG increased, and the power of low-alpha and theta waves decreased, relative to the sham group. RR Interval and QRS interval showed a significant association with MBS stimulation. Heart rate variability features showed no significant difference between the two groups. A wearable MBS modality may be feasible for use in biomedical research; the MBS can modulate the neurological and cardiovascular responses to cognitive workload.
Open AccessQuantifying Physiological Biomarkers of a Microwave Brain Stimulation Device
Physiological signals are immediate and sensitive to neural and cardiovascular change resulting from brain stimulation, and are considered as a quantifying tool with which to evaluate the association between brain stimulation and cognitive performance. Brain stimulation outside a highly equipped, clinical setting requires the use of a low-cost, ambulatory miniature system. The purpose of this double-blind, randomized, sham-controlled study is to quantify the physiological biomarkers of the neural and cardiovascular systems induced by a microwave brain stimulation (MBS) device. We investigated the effect of an active MBS and a sham device on the cardiovascular and neurological responses of ten volunteers (mean age 26.33 years, 70% male). Electroencephalography (EEG) and electrocardiography (ECG) were recorded in the initial resting-state, intermediate state, and the final state at half-hour intervals using a portable sensing device. During the experiment, the participants were engaged in a cognitive workload. In the active MBS group, the power of high-alpha, high-beta, and low-beta bands in the EEG increased, and the power of low-alpha and theta waves decreased, relative to the sham group. RR Interval and QRS interval showed a significant association with MBS stimulation. Heart rate variability features showed no significant difference between the two groups. A wearable MBS modality may be feasible for use in biomedical research; the MBS can modulate the neurological and cardiovascular responses to cognitive workload.
- Open Access
- 17 February 2021
- Published by MDPI AG in Applied Sciences
- Vol. 11 (4) , 1761
Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (), beta (), gamma (), delta (), and theta () as well as the low , high , and to ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future.- 17 February 2021
- Published by MDPI AG in Applied Sciences
- Vol. 11 (4) , 1761
Open AccessMachine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (), beta (), gamma (), delta (), and theta () as well as the low , high , and to ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future.