I am an associate professor at the Electrical and Computer Engineering Department, Michigan State University. I am also affiliated with the Communications and Signal Processing Lab at
My research focuses on the theory and applications of statistical signal processing, in particular non-stationary signal analysis. I have worked on three major areas: 1) transform based signal processing with an emphasis on sparsity-constrained feature extraction and classification; 2) signal processing methods for studying functional brain networks; and 3) statistical signal processing for fault diagnosis and prognosis for electrical drives.
More recently, my research focuses on the study of the functional networks in the brain. This research addresses the problem of quantifying the interactions across the brain by developing a comprehensive signal processing framework to study the functionality of the brain as a complex system based on electroencephalogram (EEG) data for a better understanding of psychopathologies.
· Functional mapping and control of the visual cortex: toward cortically-based visual neuroprosthesis, Michigan State University Foundation, co-PI (PI: W. Li), 09/01/14-08/31/17.
· A. Ozdemir, M. Bolanos, E. M. Bernat and S. Aviyente, “Hierarchical Spectral Consensus Clustering for Group Analysis of Functional Brain Networks,” IEEE Transactions on Biomedical Engineering, in press, 2015.
· T. P. Moran, E. M. Bernat, S. Aviyente, H. S. Schroder and J. Moser, “Sending Mixed Signals: Worry is associated with enhanced initial error processing but reduced call for subsequent cognitive control,” Social, Cognitive and Affective Neuroscience, in press, 2015.
· R. K. Singleton II, E. G. Strangas and S. Aviyente, “Extended Kalman Filtering for Remaining Useful Life Estimation of Bearings,” IEEE Transactions on Industrial Electronics, vol. 62, no. 3, pp. 1781-1790, 2015.
· Y. Liu, J. Moser and S. Aviyente, “Community detection for directional neural networks inferred from multichannel multi-subject EEG data,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 7, pp. 1919-1930, 2014.
· A. Y. Mutlu, E. M. Bernat and S. Aviyente, “A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification,” Computational and Mathematical Methods in Medicine, 2012.
· Y. Liu and S. Aviyente, “Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information,” Computational and Mathematical Methods in Medicine, 2012.
· S. Aviyente and A. Y. Mutlu, “A Time-Frequency Based Approach to Phase and Phase Synchrony Estimation,” IEEE Transactions on Signal Processing, vol. 59, no. 7, pp. 3086-3098, 2011.
· S. Aviyente, E. M. Bernat, W. S. Evans and S. R. Sponheim, “A phase synchrony measure for quantifying dynamic functional integration in the brain," vol. 32, no. 1, pp. 80-93, Human Brain Mapping, 2011.
· S. R. Sponheim, K. A. McGuire, S. S. Kang, N. D. Davenport, S. Aviyente, E. M. Bernat and K. Lim, "Evidence of disrupted functional connectivity in the brain after combat-related blast injury," vol. 54, pp. S21-29, Neuroimage, 2011.
· Ke Huang, Ph.D. 2007, currently at Google
· ECE 202, Circuits and Systems II (Spring 2011)
· ECE 280, Mathematical Methods in Electrical Engineering (Fall 2013, 2014)
· ECE 366, Introduction to Signal Processing (Fall 2009)
· ECE 457, Communication Systems (Spring 2005)
· ECE 466, Digital Signal Processing and Filter Design (Fall 2010, 2011)
· ECE 866, Time-Frequency and Wavelet Analysis (Spring 2012, 2014)