Current research description

EEG-based brain-computer interfaces
Brain computer or machine interfaces is a novel communication paradigm that aims to provide machine control and communication capabilities by interfacing with human's nervous system directly. Brain computer interfaces have progressed rapidly over the years relying on successes in a variety of basic and applied research fields from machine learning to neuroscience. Some of the recent breakthroughs are the realization of computer and robotic manipulator control by invasive and noninvasive brain activity imaging means. I focus on the development of brain computer interfaces based on electroencephalographic brain activity imaging. This project aims to realize a high-performance brain computer interface for robotic manipulator control and is supported by TUBITAK ARDEB 1001 grant No 113E611, the Science Academy (Bilim Akademesi, Turkey) BAGEP Young Scientist Award, and Toros University BAP grant No TUBAP135001.

Publications:
Mishchenko Y (2017) "Developing a 3- to 6-state slow cortical potentials Brain-Computer Interface for high performance control of a 3D robotic manipulator" (talk), NeuroTalk2017
Mishchenko Y at al. (2016) "Characterization of key properties of electroencephalographic signal for noninvasive brain machine/computer interface applications" (poster), SFN2016
Ozbay E et al. (2016) "Control of a virtual robotic hand manipulator using a non-invasive EEG-based brain-machine interface" (talk), IMSEC2016
Mishchenko Y (2016) "Developing computational infrastructure for an EEG-based brain computer interface" (talk), SIU2016
Mishchenko Y, Kaya M (2016) "Detecting an operator's attention state in a continuous passive control task using a portable electroencephalographic brain-computer interface" (preprint)
Mishchenko Y, Kaya M, Comert M (2016) "A Brain-Computer Interface detection of right and left hand movement imageries from EEG data using the SVM machine learning method" (preprint)
Yanar H, Mishchenko Y (2016) 24th IEEE Signal Processing and Communications Applications Conference (SIU) 2016
Kaya M, Yanar H, Mishchenko Y (2016) 24th IEEE Signal Processing and Communications Applications Conference (SIU) 2016
Mishchenko Y, Kaya M (2015) 23rd IEEE Signal Processing and Communications Applications Conference (SIU) 2015

Empirical characterization of the structure of neural circuits in the brain
I am interested in large-scale or high-throughput methods for empirically elucidating the structure and the function of neuronal circuits in the brain by means of anatomical as well as functional imaging approaches.

Publications:
Mishchenko Y (2016) "Recent Advances in Neural Connectivity Inference Problem for Very Large Scale Population Calcium Imaging." (book chapter) Neuroimaging SM E-books
Mishchenko Y (2016) Journal of Computational Neuroscience, 41, 158
Mishchenko Y (2015) "Reconstructing functional neural circuits with single cell resolution: statistical methods for inferring neural network topology from large scale neural activity imaging data" (talk) Janelia Research Campus)
Marblestone A, et al. (2014) arXiv:1404.5103
Rah J-C, et al. (2013) Frontiers in Neural Circuits, 7, 177
Marblestone A, et al. (2013) BioRxiv 001214
Mishchenko Y, Paninski L (2012) Journal of Computational Neuroscience, 33, 371
Mishchenko Y (2011) Journal of Neuroscience Methods 196, 289
Mishchenko Y, Vogelstein J, Paninski L (2011) Annals of Applied Statistics, 5, 1229
Mishchenko Y, Paninski L (2011) Annals of Applied Statistics, 5, 1893
NETFIT package - Fitting neural networks to population calcium imaging data, used in the above (Mishchenko, Vogelstein, and Paninski, AOAS 2011).
River-Alba M, et al. (2011) Current Biology, 21, 2000
Mishchenko Y, et al. (2010) Neuron, 67, 1009
Mishchenko Y (2010) PLoS ONE 5: e8853
Mishchenko Y (2009) J of Neurosci Methods 176, 276

Intelligent communications
I am also interested in "intelligent" approaches for communications in highly connected online communities. One of my such projects - Evion - was a "smart" message routing technology for Twitter, similar to more lately introduced Twitter Suggestions, Google Priority Inbox, Facebook Smart Feed, etc. Evion was concerned with discovering and serving in real-time personalized feeds of high-relevancy tweets to subscribers, connecting Twitterers with interesting tweets that otherwise would stay outside their usual Twitter's circle of interest. The project operated from a series of Twitter accounts (still accessible from my Evion website below) relying on @-mentions and machine learning and crowd filtering techniques for selecting individual messages. The project operated in August 2011-May 2012 and gathered over 6500 followers. Due to technical difficulties with Twitter, unfortunately, the project had shut down in May 2012. MyScienceHighlights is another project from this category. This project was designed to help academics stay in touch with the developments in periodic literature in their research areas. The project aggregates Table of Contents of over 2000 journals in 200 academic disciplines, and selects the articles for its subscribers based on individual preference criteria, serving those via email or RSS. MyScienceHighlights is in operation since February of 2011 and is currently operational.

References:
MyScienceHighlights project
Evion project

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