Research Objective

Develop and apply signal processing, image processing, analog and digital electronics, mathematical modeling, communication and system control methods to solve biomedical problems.

These projects were supported by:
NIH R41 NS36888
NIH R42 NS36888
NIH R21 NS39047
NIH 1-R01 NS/MH38494
NIH 2-R01-NS/MH38494
Screw electrode
Although the electroencephalogram (EEG) provides a unique window to observe functional activity within the brain, the manual procedures for affixing electrodes on the scalp are long and tedious. This project aims at developing a novel EEG electrode which 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.
Epilepsy is a common and frequently disabling neurological disorder. Between 0.6% and 1% of Americans have been diagnosed with epilepsy, and approximately 10% of the general population in the United States will experience a single seizure during their life time. The objective of this project is to extract and analyze early ictal activity in subdural electroencephalogram (sEEG) of epilepsy patients.
Top: A raw subdural EEG segment of 15 s containing the initial part of an epileptic seizure. Only 8 traces of the original 76 traces of data are shown.
Bottom: Extracted ictal component from the raw data in the top panel (plotted in a different scale). The circles denote automatically detected time points of seizure onset at different electrode sites.
Each electrode site is plotted as a small square which is color-coded to represent the time of seizure onset.
Multi-channel EEG compression
Recording high-resolution Electroencephalograms (EEGs) from a large number of electrodes has become a clear trend in both brain research and clinical diagnosis. However, the size of the output EEG data file increases enormously as the number of recording channels increases, causing various problems including high costs in data analysis, database management, archiving, and transmission through the internet. This project seeks to solve this problem through fundamental research on data compression specifically for EEG data, but applicable to other physiological data as well.
Search algorithm for the next node point y(j) from the current node point y(i).
An EGG segment (solid line) and node points of the piecewise linear approximation signal (small circles).
MEG signal extraction
We introduced spatial filtering methods in the spherical harmonics domain for constraining magnetoencephalographic (MEG) multichannel measurements to any user-specified spherical region of interest (ROI) inside the head. The main idea of the spatial filtering is to emphasize those signals arising from an ROI, while suppressing the signals coming from outside the ROI. We exploit a well-known method called the signal space separation (SSS), which can decompose MEG data into a signal component generated by neurobiological sources and a noise component generated by external sources outside the head. The novel methods presented in this work, expanded SSS (exSSS) and generalized expanded SSS (genexSSS) utilize a beamspace optimization criterion in order to linearly transform the inner signal SSS coefficients to represent the sources belonging to the ROI. The filters mainly depend on the radius and the center of the ROI. The simplicity of the derived formulations of our methods stems from the natural appropriateness to spherical domain and orthogonality properties of the SSS basis functions that are intimately related to the vector spherical harmonics.
ExSSS scheme: The ROI is represented by the black sphere.
GenexSSS scheme: The ROI is represented by the red sphere. The boundary between internal space and external space is represented by a red dashed circle and the virtual sphere is represented by the inside part of the blue dashed circle.
Experiment results using a rewards-based stimulation paradigm. In this paradigm, each trial proceeds as follows. The user views their current wager, is asked whether they wish to bet the entire wager for double-or-nothing (if win, wager doubles; if lose, lose entire wager) or pocket current winnings, and then is shown the outcome of the die roll.
Top Left: 102 MEG magnetometer traces ranging from the 500 ms preceeding a decision to the 800 ms following. This data has been processed by bandpass filter, averaging, but without the exSSS processing. Note the lack of any significant visible activity.
Top Right: The same data processed with the exSSS algorithm. Note the presence of significant activity between 200ms and 350ms that was uncovered by the exSSS method.

  1. Sun M, Scheuer ML, Sclabassi RJ. "Extraction and analysis of early ictal activity in subdural electroencephalogram". Ann Biomed Eng. 2001;29(10):878-886.
  2. Liu Q. "New change detection models for object-based encoding of patient monitoring video". Ph.D. Thesis: Department of Electrical and Computer Engineering, University of Pittsburgh; Advisor:Sun M. 2005.
  3. Sun M, Shi Y, Q.Liu, Sclabassi RJ. "Data integration for medical information management". Journal of VLSI Signal Processing. 2005;41:319-328.
  4. Ozkurt TE, Sun M, Sclabassi RJ. "Decomposition of magnetoencephalographic data into components corresponding to deep and superficial sources". IEEE Trans Biomed Eng. 2008;55(6):1716-1727.
  5. Kanal EY, Sun M, Ozkurt TE, Jia W, Sclabassi R. "Magnetoencephalographic imaging of deep corticostriatal network activity during a rewards paradigm". in Conf Proc IEEE Eng Med Biol Soc; September 2009; Minneapolis, MN.pp.2915-2918.
  6. Ozkurt TE, Sun M, Jia W, Sclabassi RJ. "Spatial filtering of MEG signals for user-specified spherical regions". IEEE Trans Biomed Eng. 2009;56(10):2429-2438.
  7. Alba N. A., Sclabassi R. J., Sun M., and Cui X. T., "Novel hydrogel-based preparation-free EEG electrode," IEEE Trans Neural Syst Rehabil Eng, vol. 18, pp. 415-423, 2010
  8. Sun M., Jia W., Liang W., and Sclabassi R. J., "A low-impedance, skin-grabbing, and gel-free EEG electrode," in Proc. of 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, August 28- September 1, 2012, pp. 1992-1995
  9. Sun M., Sclabassi R., Liang W., and Marcanio J., "Skin screw electrode " U. S. Patent No. 8,112,139, issued by USPTO on Feb 7, 2012, University of Pittsburgh
These projects were supported by:
Computational Diagnostics Inc., Pittsburgh PA (
Surgical simulation
Neurosurgery can now be performed through the nose trails by endoscopic imaging and surgical tools. Training the surgeons for this type of neurosurgery is very difficult. Computer simulation provides a power approach to simulate different operations on soft tissue (e.g., cutting) in a virtual environment.
Simulation results of cutting and bleeding.
3D surgical landscape reconstruction in endonasal neurosurgery
The 2D flat images currently used in endoscopic systems do not provide depth information. Surgeons have learned to depend on their visual experience and haptic feedback through the surgical instruments to compensate for this lack of depth information. We have been investigating the development of a computational solution to this problem based on the principles of computational stereoscopy. In this approach successive video frames obtained from a moving endoscope provide views of the operative site from distinct viewpoints and if the surface structure does not change during the interval between video frames, its 3D properties may be reconstructed.
Remote consulting and monitoring of robotic surgery

  1. Liu Q, Sclabassi RJ, Yao N, Sun M. "3D construction of endoscopic images based on computational stereo ". in Proc IEEE 32th Northeast Biomedical Engineering Conference; April 1-2, 2006; Eaton, PA.pp.69-70.
  2. Yuan Z, Zhang D, Yin Q, Liu Q, Shi D, Sun M. "Endoscopic image classification based on DWT-CM and improved BNN for furgical tool appearances". in Prof of 2007 International Conference on Machine Learning and Cybernetics; Aug 19 - 22, 2007; Hotel Miramar, Hong Kong, China.pp.394-397.
  3. Yuan Z, Feng S, Hu J, Kassam A, Sclabassi RJ, Sun M. "Biological tissue bleeding simulation based on CFD for endoscopic surgical training,". in Proc IEEE 34th Northeast Biomedical Engineering Conference; 2008 April 4-6, 2008; Providence, RI.
  4. Si W., Yuan Z., Liao X., Yuan Z., Sun M., Hu P., and Zhao J., "An energy based free boundary asynchronous diffusion model for 3d warping of tissue dynamics," Journal of Statistical Computation and Simulation, vol. 84, pp. 1280-1296, 2014.
These projects were supported by:
NIH 8R01EB002099
US ARMY SBIR A2-2094, Phase II
Computational Diagnostics Inc.,
Volume conduction for wireless energy delivery and communication
Recent advances in neuroscience, microelectronics, and information technology have allowed construction of miniature, but highly intelligent, devices to be implanted within the brain to perform in vitro diagnostic and therapeutic functions. However, there exists a significant problem in establishing an effective wireless data communication link between brain implants and external computer. This project investigates this link and presents a new design using the mechanism of volume conduction of biological tissues for wireless energy delivery and communication.
Wireless data communication establishes a closed-loop information link bypassing the damaged spine (marked by “XXX”).
(left) Artificial current sources of conventional design. (middle) New x antenna design. (right) Actual construction of the antenna.
Tissue modeling for electrical current conduction
Our group has developed volume conduction as a platform technology to delivers power and information to implantable devices. To completely engineer this technology, an accurate skin model must be used. We have explored two kinds of models, X model and X-Δ model to simulate the electrical current conduction of skin. These models can be used in the design and optimization of implantable volume conduction systems, which typically use higher frequencies on the order of tens to hundreds of kilohertz and voltages on the level of one to ten volts.
Left: Structural model of the volume conduction system with arrows showing current paths. Right: Circuit model ("X-Model") of the system with lumped impedances representing contributions of the skin and electrode contacts.
Left: FEA model of the skin-electrode system with ±5-volt stimulus on the “external” (left) electrodes and a passive load on the “internal” (right) side. Arrows represent current density magnitude and direction. Middle: Zoomed in view of the bottom part of the model, clearly showing the distribution of current between electrodes and the presence of a coupling current separate from the vertical cross current through the skin (Zt vs. Zc). Right: X-Δ model of the volume conduction electrode-skin system.
Wireless energy transfer
Witricity is a newly developed technique for wireless energy transfer. This project is to develop a witricity system to power medical sensors and implantable devices. New witricity resonators have been designed for both energy transmission and reception and animal model was utilized to evaluate in vivo energy transfer.
Wireless electricity resonator design
Thin-film belt shaped resonator: (a) cylindrical structure; (b) transmitter resonator; (c) receiver resonator; (d) and (e) planar resonator
Wireless energy transfer for body sensor network and implantable devices
Animal experiment on a laboratory pig. Left: implantation of our receiver. Right: power transfer to implanted receiver.
Biofuel cells
Our research group is interested in investigating the possibility of utilizing the body’s own biochemical energy stores to generate electricity in-vivo for low power implantable diagnostic and therapeutic device applications. Cyclic voltammetry is an electrochemical technique widely used for the analysis of chemical reactions. It can often reveal important thermodynamic and kinetic information about chemical processes (oxidation and reduction reactions) that occur in solution or at the electrode surface. In this study, the oxidation-reduction activity of isolated human white blood cells (WBCs) is investigated.
Schematic drawing showing possible chemical reactions occurring at the anode and cathode of a biofuel cell incorporating white blood cells at the anode. Release of serotonin (5-hT) from activated white blood cells may be followed by oxidation of the neurotransmitter to 5-hydroxyindoleacetic acid (5-hIAA). The dotted line represents the proton exchange membrane used to separate the anode and cathode compartments. The objects labeled V, R, and A in the external circuitry represent the voltmeter resistor and ammeter, respectively. Fe(II) = reduced form of potassium ferricyanide; Fe(III) = oxidized form of potassium ferricyanide.
Sampling and signal theory for application to implantable devices
Recently, information technology and microelectronics have enabled implanting miniature and highly intelligent devices within the brain for in-vitro diagnostic and therapeutic functions. Power and physical size constraints of these devices necessitate novel signal processing methods. In this project we investigate an effective data acquisition and reconstruction method for brain implants based on Asynchronous Sigma Delta Modulators (ASDMs). The ASDMs are analog non-linear feedback systems capable of time coding signals. The proposed reconstruction algorithm is based on the Prolate Spheroidal Wave Function (PSWF) expansion of the sinc functions and the order of expansion is given by the input signal being coded.
Asynchronous sigma delta modulator
(a) reconstructed vs. original EEG, (b) reconstruction error
  1. Sun M, Mickle M, Liang W, Liu Q, Sclabassi RJ. "Data communication between brain implants and computer". IEEE transactions on neural systems and rehabilitation engineering. 2003;11(2):189-192.
  2. Justin GA, Zhang Y, Cui X, Sun M, Sclabassi RJ. "Serotonin (5-HT) Released by Activated White Blood Cells in a Biological Fuel Cell Provide a Potential Energy Source for Electricity Generation". in IEEE Int Conf of Engineering in Medicine and Biology Society; Aug.30-Sept.3, 2006; New York City, NY.
  3. Sclabassi RJ, Liu Q, Hackworth SA, Justin GA, Sun M. "Platform technologies to support brain-computer interfaces". Neurosurgery Focus. 2006;20(5):1-13.
  4. Sun M, Hackworth SA, Tang Z, Zhao J, Li D, Enos SE, Errigo B, Gilbert G, Marchessault R, Cardin S, Turner T, Sclabassi RJ. "Design of the next-generation medical implants with communication energy and ports". Stud Health Technol Inform. 2007;125:457-459.
  5. Sun M, Hackworth SA, Tang Z, Gilbert G, Cardin S, Sclabassi RJ. "How to pass information and deliver energy to a network of implantable devices within the human body". in Conf Proc IEEE Eng Med Biol Soc; August 23-26, 2007; Lyon, France.pp.5286-5289.
  6. Senay S, Chaparro LF, Sclabassi RJ, Sun M. "Time encoding and reconstruction of multichannel data by brain implants using asynchronous sigma delta modulators". in Conf Proc IEEE Eng Med Biol Soc; 2009 Sept. 3-6; Minneapolis, MN.pp.6893-6896.
  7. Zhang F, Hackworth SA, Liu X, Chen H, Sclabassi RJ, Sun M. "Wireless energy transfer platform for medical sensors and implantable devices". in Conf Proc IEEE Eng Med Biol Soc; 2009 Sept. 3-6, 2009; Minneapolis, MN.pp.1045-1048.
  8. Zhang F, Hackwoth SA, Liu X, Li C, Sun M. "Wireless power delivery for wearable sensors and implants in Body Sensor Networks". in Conf Proc IEEE Eng Med Biol Soc; 2010 Aug 31-Spet 4, 2010; Buenos Aires, Argentina.pp.692-695.
  9. Hackworth SA. "Design, optimization, and implementation of a volume conduction energy transfer platform for implantable devices". Ph.D. Thesis: Department of Electrical and Computer Engineering, University of Pittsburgh; Advisor: Sun M. 2010.
  10. Wang J., Ho S. L., Fu W. N., and Sun M., "FEM simulations and experiments for the advanced witricity charger with compound nano-TiO2 interlayers," IEEE Transactions on Magnetics, vol. 47, pp. 4449-4452, 2011.
  11. Zhang F., Hackworth S. A., Fu W., Li C., Mao Z.-H., and Sun M., "Relay effect of wireless power transfer using strongly coupled magnetic resonances," IEEE Transactions on Magnetics, vol. 47, pp. 1478-1481, 2011.
  12. Wang J., Ho S. L., Fu W. N., and Sun M., "Analytical design study of a novel witricity charger with lateral and angular misalignments for efficient wireless energy transmission," IEEE Transactions on Magnetics, vol. 47, pp. 2616-2619, 2011.
  13. Ho S. L., Wang J., Fu W. N., and Sun M., "A novel resonant inductive magnetic coupling wireless charger with TiO2 interlayer," Journal of Applied Physics, vol. 109, p. 07E502, 2011.
  14. Wang J. H., Li J. G., Ho S. L., Fu W. N., Zhao Z. G., Yan W. L., and Sun M. G., "Analytical study and corresponding experiments for a new resonant magnetic charger with circular spiral coils," Journal of Applied Physics, vol. 111, p. 07E704, 2012.
  15. Wang J., Li J., Ho S. L., Fu W. N., Zhao Z., Yan W., and Sun M., "Analytical study and corresponding experiments for a new resonant magnetic charger with circular spiral coils," Journal of Applied Physics, vol. 111, p. 07E704, 2012.
  16. Wang J. H., Li J. G., Ho S. L., Fu W. N., Li Y., Yu H. L., and Sun M. G., "Lateral and angular misalignments analysis of a new PCB circular spiral resonant wireless charger," IEEE Transactions on Magnetics, vol. 48, pp. 4522-4525, 2012.
  17. Zhang F. and Sun M., "Wireless power transfer with strongly coupled magnetic resonance," in Wireless Power Transfer, J. I. Agbinya,Eds. Gistrup, Denmark: River Publishers, 2012.
  18. Yin N., Xu G., Yang Q., Zhao J., Yang X., Jin J., Fu W., and Sun M., "Analysis of wireless energy transmission for implantable device based on coupled magnetic resonance," IEEE Transactions on Magnetics, vol. 48, pp. 723-726, 2012.
  19. Wang J. H., Li J. G., Ho S. L., Fu W. N., Li Y., Yu H. L., and Sun M. G., "Lateral and angular misalignments analysis of a new PCB circular spiral resonant wireless charger," IEEE Transactions on Magnetics, vol. 48, pp. 4522-4525, 2012.
  20. Xu Q., Wang H., Gao Z. L., Mao Z. H., He J. P., and Sun M. G., "A novel mat-based system for position-varying wireless power transfer to biomedical implants," IEEE Transactions on Magnetics, vol. 49, pp. 4774-4779, 2013.
  21. Xu Q., Gao Z., Wang H., He J., Mao Z.-H., and Sun M., "Batteries not included: a mat-based wireless power transfer system for implantable medical devices as a moving target," IEEE Microwave Magazine, vol. 14, pp. 63-72, 2013.
  22. Sun M., Hackworth S., Sclabassi R. J., Zhang F., and Liu X., "Wireless Power Transfer System," U.S. Patent No. 8,421,274, Priority date: Sep 12, 2008; Publication date: Mar 6, 2014.
  23. Zhang F. and Sun M., "Efficient wireless power transfer based on strongly coupled magnetic resonance," in Intelligent Wireless Power Transfer Systems: Theory and Practice Gistrup, Denmark: River Publishers, 2015.
Hand Tracking for Information Delivery
The aim of this project is to develop a simple device that allows a wearable computer to know motions and postures of the hands.
These projects were supported by:
NSF 0727256
New hand tracking device
Hand posture reconstruction
Reconstructed hand posture during extend-flex movement of the index finger. The real value is obtained using a Motion Tracking System from Vicon
Potential Applications
  1. Mao ZH, Lee HN, Sclabassi RJ, Sun M. "Information capacity of the thumb and the index finger in communication". IEEE Trans Biomed Eng. 2009;56(5):1535-1545.
  2. Ma Y, Jia W, Li C, Yang J, Mao ZH, Sun M. "Magnetic hand motion tracking system for human-machine interaction". Electron Lett. 2010;46(9):621-623.
  3. Vinjamuri R., Sun M., Chang C. C., Lee H. N., Sclabassi R. J., and Mao Z. H., "Temporal postural synergies of the hand in rapid grasping tasks," IEEE Trans Inf Technol Biomed, vol. 14, pp. 986-994, 2010
  4. Ma Y., Mao Z.-H., Jia W., Li C., Yang J., and Sun M., "Magnetic hand tracking for human-computer interface," IEEE Trans. Magn., vol. 47, pp. 970-973, 2011.