Selin Aviyente


Department of Electrical and Computer Engineering

Michigan State University, East Lansing, MI 48824.

(517) 355 7649


I am a professor at the Electrical and Computer Engineering Department, Michigan State University.  I am also affiliated with the Communications and Signal Processing Lab at Michigan State University (CSP Group).



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.




  • Sparse Representations for Classification (


Current Projects


·         CIF: Small: Low-Dimensional Structure Learning for Tensor Data with Applications to Neuroimaging, NSF, PI (co-PI: M. Iwen, Math), $500,000, 07/01/16-06/30/19.

·         Cognitive Control in Anxiety: The Role of Ovarian Hormones, NIH, co-PI (PI: J. Moser), $3,381,819, 3/25/2016 - 2/28/2021.

·         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.


Selected Recent Publications 


Tensor Decomposition and Applications:

·         A. Ozdemir, E. M. Bernat and S. Aviyente, “Recursive Tensor Subspace Tracking for Dynamic Brain Network Analysis,” IEEE Transactions on Signal and Information Processing on Networks, in press, February 2017.

·         A. G. Mahyari, D. Zoltowski, E. M. Bernat and S. Aviyente, “A tensor decomposition based approach for detecting dynamic network states from EEG,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 1, pp. 225-237, 2017.


Phase Synchrony and Functional Connectivity:


·         M. Al-Khassaweneh, M. Villafane-Delgado, A. Y. Mutlu, and S. Aviyente, “A Measure of Multivariate  Phase Synchrony using Hyperdimensional Geometry,” IEEE Transactions on Signal Processing, pp. 2774-2787, vol. 64, no. 11, 2016. 

·            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, 10 (11), pp. 1548-1556, 2015.

·         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, vol. 62, no. 9, pp. 2158-2169, 2015.

·         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.


Directed Information:
·          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.

·         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.


Signal Processing Approaches in Fault Diagnosis:

·         R. K. Singleton II, E. G. Strangas and S. Aviyente, “The Use of Bearing Currents and Vibrations in Lifetime Estimation of Bearings,” IEEE Transactions on Industrial Informatics, in press, 2017.

·         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.


Current Students


·         Alp Ozdemir (Ph.D.)

·         Esraa Al-Sharoa (Ph.D.)

·         Marisel Villafane-Delgado (Ph.D.)

·         Arash Mahyari (Ph.D.)


Past Students


·         Ke Huang, Ph.D. 2007, currently at Google

·         Mahmood Al-Khassaweneh, Ph.D. 2007, currently Associate Professor at Yarmouk University, Jordan

·         Zeyong Shan, Ph.D. 2008, currently at Safety Vision

·         Jacob Swary, M.S. 2007

·         Westley Evans, M.S. 2008

·         Marcos Bolanos, Ph.D. 2012, currently at CNA

·         Ying Liu, Ph.D. 2012, currently at Bosch, Pittsburgh, PA

·         Ali Yener Mutlu, Ph.D. 2012, currently Assistant Professor at Katip Celebi University, Izmir, Turkey

·         Suhaily Cardona, M.S. 2012

·         Rodney Singleton, Ph.D. 2016, currently at Applied Physics Lab, Baltimore, MD


Recent Teaching

·         ECE 202, Circuits and Systems II (Spring 2011)

·         ECE 280, Mathematical Methods in Electrical Engineering (Spring 2017)

·         ECE 366, Introduction to Signal Processing (Fall 2015)

·         ECE 457, Communication Systems (Spring 2005)

·         ECE 466, Digital Signal Processing and Filter Design (Fall 2010, 2011, Spring 2017)

·         ECE 866, Time-Frequency and Wavelet Analysis (Spring 2012, 2014, 2016)