These projects were supported by:
NIH R41 NS36888
NIH R42 NS36888
NIH R21 NS39047
NIH 1-R01 NS/MH38494
NIH 2-R01-NS/MH38494

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.

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.

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.

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 (www.cdi.com)

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.

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.

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 W81XWH-05-C-0047
US ARMY SBIR A2-2094, Phase II
Computational Diagnostics Inc.,

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.

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.

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 Wireless energy transfer for body sensor network and implantable devices

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.

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.

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.

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

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.