Mingui Sun, PhDProfessor
Mingui Sun, PhD, received a BS degree in instrumental and industrial automation in 1982 from the Shenyang Chemical Engineering Institute in Shenyang, China, and an MS degree in electrical engineering in 1986 from the University of Pittsburgh, where he also earned a PhD degree in electrical engineering in 1989. He was later appointed to the faculty in the Department of Neurological Surgery.
Dr. Sun’s research interests include neurophysiological signals and systems, biosensor designs, brain-computer interface, bioelectronics and bioinformatics. He has more than 400 publications.
Specialized Areas of Interest
Biomedical engineering; biomedical instrumentation; biomedical signal processing, computational neurophysiology, image and video processing; computer-assisted neuro-surgery and diagnosis.
Professional Organization Membership
American Institute for Medical and Biological Engineering
Institute of Electrical and Electronics Engineers
IEEE Engineering in Medicine and Biology Society
IEEE Circuit and Systems Society
Education & Training
BS, Instrumentation/Industrial Automation, Shenyang Chemical Institute, 1982
MS, Electrical Engineering, University of Pittsburgh, 1986
PhD, Electrical Engineering, University of Pittsburgh, 1989
Li Z, Jia W, Chen H-C, Wang K, Zuo W, Meng D, Sun M. Multiview Stereo and Silhouette Fusion via Minimizing Generalized Reprojection Error. Image and Vision Computing 33:1-14, 2015.
Chen H-C, Jia W, Sun X, Li Z, Li Y, Fernstrom JD, Burke LE, Baranowski T, Sun M. Saliency-aware food image segmentation for personal dietary assessment using a wearable computer. Measurement Science and Technology 26(2), 2015.
Li Z, Wei Z, Yue Y, Wang H, Jia W, Sun M. An adaptive hidden Markov model for activity recognition using a wearable multi-sensor device. Journal of Medical Systems 39:57, 2015.
Cheng F, Zhang H, Sun M, Yuan D. Cross-trees, Edge and Superpixel Priors-based Cost aggregation for Stereo matching. Pattern Recognition, 48(7):2269-2278, 2015.
Sun W, Wang H, Sun C, Guo B, Jia W, Sun M. Fast single image haze removal via local atmospheric light veil estimation. Computers & Electrical Engineering, online Jounal, March 2015.
Liao X, Yuan Z, Fai Q, Quo J, Zhen Q, Yu S, Tong Q, Si W, Sun M. Modeling and Predicting Tissue Movement and Deformation for High Intensity Focused Ultrasound Therapy,” PLoS One 10:e0127873, 2015.
Chyu MC, Austin T, Calisir F, Chanjaplammootil S, Davis MJ, Favela J, Gan H, Gefen A, Haddas R, Shen CL, Shieh JS, Su CT, Sun L, Sun M, Tewolde SN, Williams EA, Yan C, Zhang J, Zhang YT. Healthcare Engineering Defined: a White Paper. Journal of Healthcare Engineering 6(41):635-648, 2015.
Dudik JM, Coyle JL, El-Jaroudi A, Sun M, Sejdic E. A matched dual-tree wavelet denoising for tri-axial swallowing vibrations. Biomedical Signal Processing and Control 27:112-121, 2016.
A complete list of Dr. Sun's publications can be reviewed through the National Library of Medicine's publication database.
1) Wearable eButton for Evaluation of Energy Balance with Environmental Context and Behavior
In this study, Dr. Sun proposes the refinement of eButton, an electronic device that was developed under the NIH GEI diet and physical activity research program. This button-like device can be attached to clothing and worn on the chest using a pair of magnets or a pin. The new eButton will contain numerous innovative designs, including a motion sensor to detect physical activity, an optical eating detector to monitor eating/drinking/smoking, two miniature cameras that produce a stereo vision to measure food portion size without depending on a reference card. The eButton will store the multimedia data acquired by these advanced miniature sensors in a flash memory within the device. It will also have a wireless link to a smart phone which will allow researchers to monitor the operating status of eButton and communicate with subjects remotely in real time. During the first year of this research, the new eButton and associated algorithms/software is being designed and constructed in Dr. Sun’s laboratory by an experienced team of electronic/software engineers based on its early version developed under the NIH GEI diet and physical activity research program. Once eButton is constructed, we will implement a thorough validation process using human subjects to evaluate its accuracy in diet and physical activity assessment.
2) Biomimetic Self-Adhesive Dry EEG Electrodes
This biomedical engineering project aims to develop a novel skin-surface electroencephalogram (EEG) electrode. This new electrode does not require application of electrolyte; is able to penetrate scalp hair easily during electrode placement; can be quickly applied and removed; has low and stable electrode impedance; and has an extraordinary ability to self-adhere to the scalp without glue or tape. Its unconventional design is inspired from a biological system (the toe of geckos) which has shown clear effectiveness in the natural environment. In the current stage, design and construction of the electrode is being conducted and a test bed is being constructed to evaluate its performance.
3) Development and Evaluation of a Novel Wireless EEG Monitoring Sensor
This study, which has been approved for funding by the Center for Medical Innovation (CMI) at the University of Pittsburgh, aims to develop a wireless EEG system to provide critical point-of-care information about brain electrical activity. A novel dry electrode, which can be installed rapidly, is used to acquire EEG from the scalp. A wireless data link between the electrode and a data port (i.e., a smartphone) is established based on Bluetooth technology. A prototype of this system has been implemented and its performance in acquiring EEG has been evaluated. Dr. Sun’s current interest is to further improve the performance of this system while minimizing its physical dimension.