Department of Biomedical Engineering

Neuroengineering Research > Neuroengineering

  • CNA Lab.
  • CoNE Lab.
  • NEMO Lab.
  • NIS Lab.
  • SUH Lab.

Computational NeuroImage Analysis Lab. (Professor Jong Min. Lee)


Computational NeuroImage Analysis Lab. (CNA) has a goal of developing advanced neuroimage processing, analysis and interpretation, and the early detection of various neurodegenerative diseases based on the results. Main research areas cover the development of the advanced automated large scale 3-dimensional neuroimage processing and analysis algorithms, and single subject analysis system based on the large scale multimodal neuroimage database system. There are many active collaborators from the medical side including neurologists, psychiatrists and radiologists. From this kind of collaboration, we published more than 100 SCI papers and have tried to apply the developed algorithm to the real clinical cases. Also, we have collaborated with a couple of internally renowned lab such as MNI and NIH. We focused neuroimage analysis for the last 10 years and achieved two major grants NRL and NLRL. We also started to collaborate with Samsung Electronics in order to transfer our specialized knowledge to the medical products.

Aare & Major contents Major results (representative scientific papers)

Cortical parametric model development and applications

  • Development of an accurate cortical parametric model
  • Development of novel morphological parameters based on the model
  • Applications on the detection of the structural changes in the neurodegenerative diseases from the collaborations with the clinicians
  • Jun Ki Lee, Jong-Min Lee*, June Sic Kim, In Young Kim, Alan C. Evans, Sun I. Kim, A novel quantitative cross-validation of different cortical surface reconstruction algorithms using MRI phantom, Neuroimage, Vol. 31(2), p. 572-84, 2006 June
  • Kiho Im, Jong-Min Lee*, Oliver Lyttelton, Sun Hyung Kim, Alan C. Evans, Sun I. Kim, Brain Size and Cortical Structure in Adult Human Brain, Cerebral Cortex, Vol. 18(9), p. 2181~2191, 2008 September

Deep brain structure shape analysis and applications

  • Development of an advanced automatic segmentation of the deep brain structure
  • Development of an accurate parameter model
  • Development of an detection of the local changes based on the model
  • Applications on the detection of the structural changes in the neurodegenerative diseases from the collaborations with the clinicians
  • Sun Hyung Kim, Jong-Min Lee*, Hyun-Pil Kim, Dong Pyo Jang, Yong-Wook Shin, Tae Hyon Ha, Jae-Jin Kim, In Young Kim, Jun Soo Kwon, Sun I. Kim, Asymmetry Analysis of Deformable Hippocampal Model Using the Principal Component in Schizophrenia, Human Brain Mapping, Vol. 25(4), p. 361-369, 2005 August
  • Do-Hyung Kang, Sun Hyung Kim, Chi-Won Kim, Jung-Seok Choi, Joon Hwan Jang, Myung Hun Jung, Jong-Min Lee, Sun I. Kim, Jun Soo Kwon, Thalamus surface shape deformity in obsessive-compulsive disorder and schizophrenia, NeuroReport, Vol. 19(6), p. 609-613, 2008 April

Diffusion Tensor Imaging analysis and applications

  • Development of Diffusion Tensor Imaging analysis package (Samsung Electronics)
  • Development of the accurate analysis of the white matter combined with the structural image
  • Applications on the detection of the white matter changes in the neurodegenerative diseases from the collaborations with the clinicians
  • Bang-Bon Koo, Ning Hua, Chi-Hoon Choi, Itamar Ronen, Jong-Min Lee, Dae-Shik Kim, A Framework to Analyze Partial Volume Effect on Gray Matter Mean Diffusivity Measurements, Neuroimage, Vol. 44(1), p. 136~144, 2009 January
  • Jun-Sung Park, Uicheul Yoon, Ki-Chang Kwak, Sang Won Seo, Sun I. Kim, Duk L. Na and Jong-Min Lee*, The relationships microstructural properties of the midsagittal corpus callosum in human brain, NeuroImage, Vol. 56(1), p. 174-184, 2011 May

Functional MRI analysis and applications

  • Development of the analysis tool of the brain connectivity
  • Development of the automatic parcellation method based on the functional connectivity
  • Development of software for multimodal dynamic neuroimaging
  • Development of an accurate detection of the brain activities combined with the cortical parameter model
  • Applications on the detection of the brain connectivity changes in the neurodegenerative diseases from the collaborations with the clinicians
  • Hang Joon Jo, Jong-Min Lee*, Jae-Hun Kim, Chihoon Choi, Bon-Mi Gu, Do-Hyung Kang, Jeonghun Ku, Jun Soo Kwon, Sun I. Kim, Artificial shifting of fMRI activation localized by volume- and surface-based analyses, NeuroImage, Vol. 40(3), p. 1077-1089, 2008 April
  • Jae-Hun Kim, Jong-Min Lee*, Hang Joon Jo, Sook Hui Kim, Jung Hee Lee, Sung Tae Kim, San Won Seo, Robert W. Cox, Duk L. Na, Sun I. Kim, Ziad S. Saad, Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: functional connectivity-based parcellation method, NeuroImage, Vol. 49(3), p. 2375-2386, 2010 February

Early detection of the neurodegenerative disease based on the neuroimage

  • Automatic classification of Normal control /MCI / Alzheimer Diseases
  • Single subject analysis based on the large scale database system
  • High classification performance between AD and the normal control (>95%)
  • In preparation of the acquiring the neuroimage data from more than 20 hospitals

Computational Neuroengineering CoNE Lab. (Professor Chang Hwan. Im)


Computational Neuroengineering (CoNE) Labora-tory of Hanyang University was established in the Department of Biomedical Engineering in 2011. We are actively studying on advanced research topics in neuroengineering such as < Brain Computer Interface (BCI) >,< Early Diagnosis of Neuropsychiatric Diseases Based on the Analysis of Neural Signals >,< Noninvasive Brain Stimulation for Neuro-rehabilitation >, and < Multimodal Dynamic Neuroi- maging >. Our laboratory is collaborating with world-class institutes and laboratories such as Seoul National University Hospital, Severance Hospital, Korea University Medical Center, Inje University Paik Hospital, Samsung Medical Center (Korea), University of Minnesota, University of Oklahoma (US), RIKEN, and ATR (Japan). We are now performing 5 government-funded research projects and have published more than 70 SCI-indexed international journal articles.

Fields & Topics Recent research results

Brain Computer Interface (BCI)

  • Mental Speller using SSVEP
  • Vision-free Brain Computer Interfacing using selective attention to auditory stimuli
  • Control of a robot arm using P300 speller
  • Mu-rhythm based motor imagery training for brain computer interface system
  • Brain-Fingerprinting: classification of human intention using spatiotemporal characteristics of neural signals
Research Results-image1

Diagnosis of Neuropsychiatric Diseases Based on the Analysis of Neural Signals

  • Development of biomarkers for the diagnosis of neuropsychiatric diseases such as dementia, schizophrenia, ADHD, anxiety disorder, etc.
  • Investigation of mechanisms of neurological disorders using EEG source imaging
  • Human brain network analysis and its application to various neurological diseases
Research Results-image2

Noninvasive Brain Stimulation for Neural Rehabilitation

  • Estimation of optimal electrode locations of tDCS that can minimize the input current injection
  • Development of an array-type tDCS system for the image guided tDCS
  • Validation of safety issues in tDCS using 3D human body modeling and numerical analysis
  • Development of a multi-channel tDCS system
  • Analysis and design of TMS systems
Research Results-image3

Multimodal Dynamic Neuroimaging

  • New algorithm for addressing mismatch issues in the combinatory EEG-fMRI study
  • Validation of simultaneous EEG-fMRI recording in the aspect of source imaging
  • Development of software for multimodal dynamic neuroimaging
  • Development of a new algorithm for the efficient combination of EEG and MEG
Research Results-image4

Neural Engineering and Modulation Lab (Professor Dong Pyo. Jang)


Not available

Neural Information Systems Lab (Professor Anmo J. Kim)


Our research focuses on understanding neural circuit principles that could ultimately inspire new solutions for difficult engineering problems. In particular, our research programs focus on understanding sensorimotor control algorithms in flying insects. Our approach is to get at a system-level understanding of a brain function by combining various experimental techniques such as electrophysiology, calcium imaging, optogenetics, and behavioral genetics. Another line of work in the lab involves developing computational models of the experimentally-derived neural algorithms, and thereby bringing new insights into engineering applications such as unmanned air vehicles (i.e., drones).

Specific research areas Recent results

Higher-order Visuomotor Processing

We can almost effortlessly recognize others' faces from complex backgrounds and distinguish subtle differences in facial expressions. Such abilities can easily outperform state-of-the-art computer algorithms. How does a set of brain cells give rise to such a remarkable feat? We approach this question by studying the fruit fly's visual system. Owing to their premiere genetic toolkits, fruit flies have become one of the most popular model organisms in neuroscience. This tiny insect invests more than half of their nervous system into visual processing and exhibit a large repertoire of higher-order visuomotor functions, including object recognition/tracking, object avoidance, object distance estimation, selective attention, place learning, and gap width estimation. In our lab, we study how such visual features are represented in a neural circuit and triggers specific behavioral programs.

Research Results-image1

Park, H., Lee, J., Kim, A.J., Motion-independent visual computation underlies small object avoidance in flying Drosophila, CSHL Neurobiology of Drosophila meeting (2019), New York

Flight Control via Internal Predictions

Fruit flies are agile fliers. With wings that beat 200 times a second, they can hover, fly straight, avoid collision, turn rapidly, and even land on an improbable target, such as a ceiling. To make these sophisticated flight maneuvers, fruit flies constantly make use of their sensory feedback. In two previous studies (Cell 2017 and Nat Neurosci 2015), we demonstrated how a set of visual neurons guides fruit fly’s stabilization behaviors and how their visual signaling is quantitatively modulated by internal predictions to enable rapid flight turns. We aim to develop this study further by uncovering the detailed circuit mechanism of internal predictions.

Kim, A.J., Fenk, L.M., Lyu, C. and Maimon, G., 2017. Quantitative predictions orchestrate visual signaling in Drosophila. Cell, 168(1-2), pp.280-294.

Modeling Brain-inspired Predictive Control

Flies' flight abilities outperform any man-made flying device, in terms of their stability, adaptability, maneuverability, and robustness. For this reason, quantitative analyses of neural mechanisms underlying flies' flight control is likely to help developing new solutions for man-made robots. In particular, the forward model can predict sensory feedback caused by a self-generated action, and that prediction can be used to selectively pass or block the neural signals from the sensory periphery. During flight, such control mechanisms can momentarily switch off the powerful stabilization system and permit aggressive flight maneuvers. Currently, we are developing a computational model of this control mechanism to apply it to simple engineering systems, such as an inverted pendulum or an unmanned air vehicle (i.e., drone).

Research Results-image3

Smart Ubiquitous Healthcare Lab. (Professor In Young. Kim)


Ubiquitous Healthcare

Ubiquitous healthcare (U-healthcare) is the medical service for checking individual health status at anywhere and anytime using wireless telecommunication technique, so it is getting the spotlight as major part of future industry. The system of ubiquitous healthcare can acquire user’s bio-signal during free life without any limits of activity (Non-intrusive), and it gives user the information of individual health status or medical opinion that is based on continuously or periodically acquired data. In addition, when an emergency situation occurs, it will be possible to service quick medical treatment by knowing family or physician. The various U-healthcare researches are currently processing about non-intrusive and non-invasive bio-signal monitoring in Smart Ubiquitous Healthcare Lab at Hanyang University. Mainly, researches that are linked with cardiovascular system have progressed, and most of projects have focused on blood pressure, electrocardiography, physical activity, data mining, and physiological modeling.

Hearing Science

We have been studying in a variety of fields to improve the performance of the digital hearing aids that recover some hearing impaired people. Specifically, our researches focus on the digital signal processing to improve the speech enhancement. We are researching digital hearing aids with high-quality sound and are developing algorithms and designs for signal processing. Digital hearing aids provide dramatic sound enhancement, improved communication ability and unequalled flexibility to match individual user needs. We are studying the techniques (companding, wide dynamic range compression etc.) which are generally related in the digital hearing aids. Also, we are interested in training induced auditory rehabilitation. Current research theme is auditory sensitivity enhancement by auditory sound training.

Neural Engineering

arly exploration of neural systems focused on understanding how neural systems work at the cellular, tissue, and system levels, and engineering methodologies were developed to detect, process, and model these neural signals. Recently, tremendous progress has been made in the field of neural engineering, not only understanding the mechanism, detection, and processing of the neural signals, but also on restoring the impaired neural systems functions and interfacing the neural systems with artificial devices and machines.

Fields & Contents Results

Ubiquitous Healthcare

  • Blood Pressure Measurement & Applications
  • Sports Medicine & Physiology
  • Data Mining
  • Modeling and Simulation for Human Organs and Cardiovascular System
Research Results-image1

Hearing Science

  • Hearing aid research
  • Hearing psychophysics research
Research Results-image2

Neural Engineering

  • Rewiring the Brain
  • Develop Next-generation Deep Brain Stimulator
Research Results-image3