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 460 publications.
Specialized Areas of Interest
Professional Organization Membership
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(5):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):2269-78, 2015.
Sun W, Wang H, Sun C, Guo B, Jia W, Sun M. Fast single image haze removal via local atmospheric light veil estimation. Comput Electr Eng 46:371-383, 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(5):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. J Healthc Eng 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. Biomed Signal Process 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.
A Leadless EEG Sensor
Non-convulsive seizures (NCS) and non-convulsive status epilepticus (NCSE) are critical neurophysiological conditions which do not have overt clinical signs. These conductions can be diagnosed only with EEG monitoring. Unfortunately, approximately 2% of the patients in the ICU undergo continuous EEG monitoring. Primary reasons for the underuse of this technology is due to the complexity in setting up EEG equipment in busy, human resource constrained ICU. Dr. Sun is developing a self-contained EEG sensor in the size of a U.S. quarter with no electrode leads. By simply pressing the EEG sensor against the unprepared scalp and twisting slightly, the device can grasp the skin firmly and start acquiring and transmitting EEG wirelessly to a bedside monitor, a smartphone, a tablet, or a Bluetooth enabled device within an ambulance. With these unique features, the aforementioned problem can be solved.
A Human-Mimetic AI System for Automatic, Passive and Objective Dietary Assessment
Unhealthy diet is strongly linked to risks of chronic diseases, such as cardiovascular diseases, diabetes and certain types of cancer. Unhealthy foods with large portion sizes are widely consumed. Currently, 68.5% of U.S. adults are overweight, among the highest in developed countries. Understanding how the diet-related risk factors affect people’s health and finding effective ways to empower them in improving lifestyle habits are among the most important tasks in public health. Dr. Sun has been working on a biomedical engineering project to address the dietary assessment problem, taking advantage of advanced mathematical modeling, wearable electronics and artificial intelligence.