Mishchenko, Yuriy
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Mishchenko, Y
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yuriy.mishchenko@ieu.edu.tr
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05.02. Biomedical Engineering
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Former Staff
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Sustainable Development Goals
5
GENDER EQUALITY
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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13
CLIMATE ACTION
0
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8
DECENT WORK AND ECONOMIC GROWTH
0
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14
LIFE BELOW WATER
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17
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0
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1
NO POVERTY
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2
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4
QUALITY EDUCATION
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11
SUSTAINABLE CITIES AND COMMUNITIES
0
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16
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0
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3
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1
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6
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0
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12
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0
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10
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0
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15
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0
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7
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0
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Documents
38
Citations
1162
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17
Documents
34
Citations
985
Scholarly Output
9
Articles
6
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0/0
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WoS Citation Count
216
Scopus Citation Count
273
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3
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3
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0
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0
WoS Citations per Publication
24.00
Scopus Citations per Publication
30.33
Open Access Source
6
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0
| Journal | Count |
|---|---|
| 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 | 1 |
| Bilgisayar Bilimleri ve Mühendisliği Dergisi | 1 |
| European Physıcal Journal C | 1 |
| Expert Systems Wıth Applıcatıons | 1 |
| Ieee Transactıons on Bıomedıcal Engıneerıng | 1 |
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Now showing 1 - 9 of 9
Article Citation - WoS: 30Citation - Scopus: 39Developing a Three- To Six-State Eeg-Based Brain-Computer Interface for a Virtual Robotic Manipulator Control(IEEE-Inst Electrical Electronics Engineers Inc, 2019) Mishchenko, Yuriy; Kaya, Murat; Ozbay, Erkan; Yanar, HilmiObjective: We develop an electroencephalography (EEG)-based noninvasive brain-computer interface (BCI) system having short training time (15 min) that can be applied for high-performance control of robotic prosthetic systems. Methods: A signal processing system for detecting user's mental intent from EEG data based on up to six-state BCI paradigm is developed and used. Results: We examine the performance of the developed system on experimental data collected from 12 healthy participants and analyzed offline. Out of 12 participants 3 achieve an accuracy of six-state communication in 80%-90% range, while 2 participants do not achieve a satisfactory accuracy. We further implement an online BCI system for control of a virtual 3 degree-of-freedom (dof) prosthetic manipulator and test it with our three best participants. Two participants are able to successfully complete 100% of the test tasks, demonstrating on average the accuracy rate of 80% and requiring 5-10 s to execute a manipulator move. One participant failed to demonstrate a satisfactory performance in online trials. Conclusion: We show that our offline EEG BCI system can correctly identify different motor imageries in EEG data with high accuracy and our online BCI system can be used for control of a virtual 3 dof prosthetic manipulator. Significance: Our results prepare foundation for further development of higher performance EEG BCI-based robotic assistive systems and demonstrate that EEG-based BCI may be feasible for robotic control by paralyzed and immobilized individuals.Article Citation - WoS: 73Citation - Scopus: 103Distinguishing Mental Attention States of Humans Via an Eeg-Based Passive Bci Using Machine Learning Methods(Pergamon-Elsevier Science Ltd, 2019) Aci, Cigdem Ivan; Kaya, Murat; Mishchenko, YuriyRecent advances in technology bring about novel operating environments where the role of human participants is reduced to passive observation. While opening new frontiers in productivity and lifestyle, such environments also create hazards related to the inability of human individuals to maintain focus and concentration during passive control tasks. A passive brain-computer interface for monitoring mental attention states of human individuals (focused, unfocused, and drowsy) by using electroencephalographic (EEG) brain activity imaging and machine learning data analysis methods is developed in this work. An EEG data processing pipeline and a machine learning mental state detection algorithm using the Support Vector Machine (SVM) method were designed and compared with k-Nearest Neighbor and Adaptive Neuro-Fuzzy System methods. To collect 25 h of EEG data from 5 participants, a classic EEG headset was modified. We found that the changes in EEG activity in frontal and parietal lobes occurring at 1-5 Hz and 10-15 Hz frequency bands were associated with the changes in individuals' attention state. We demonstrated the ability to use such changes to identify individuals' attention state with 96.70% (best) and 91.72% (avg.) accuracy in experimental settings using a version of continuous performance task with SVM-based mental state detector. The findings help guide the design of future systems for monitoring the state of human individuals by means of EEG brain activity data. (C) 2019 Elsevier Ltd. All rights reserved.Conference Object A passive brain-computer interface for monitoring mental attention state(Institute of Electrical and Electronics Engineers Inc., 2018) Kaya M; Aci C.; Mishchenko, YuriyOperators who use a vehicle have less control load with fast improvements of robotic and autonom systems so that situation causes losing of attention an operator while important control processes. In this paper, a passive brain computer interface for monitoring mental attention state of human individuals by using electroencephalographic (EEG) brain activity imaging is developed using a machine learning data analysis method Support Vector Machine. Also a mental state detection system using EEG data is evolved as well. It has been determined that changes in EEG activity in the frontal and parietal lobes occurring in the 1-5 Hz and 1015 Hz frequency bands are associated with changes in attention state. Such changes were detected with 90% to 95% accuracy in experimental settings. The results of the work done will guide the design of future systems to monitor the status of the operators via EEG brain activity data. © 2018 IEEE.Article Citation - WoS: 1Citation - Scopus: 1Design of an Accessible, Powered Myoelectrically Controlled Hand Prosthesis(Assoc Information Communication Technology Education & Science, 2017) Akirmak, Osman Onur; Tatar, Cagdas; Altun, Ziya Gokalp; Mishchenko, YuriyIn this paper we describe accessible myoelectric prosthetic hand design based on modification of existing mechanical prosthesis and off-the-shelf parts and components. Despite significant advances in myoelectric prosthetics, such existing devices remain out of reach of the majority of the patients needing them due to high costs and complexity. We describe a simple design that can be assembled based on existing or readily acquirable parts at approximately 1/100 of the cheapest commercially available alternative. Our design offers wrist disarticulation patients in developing countries an affordable myoelectric prosthesis with significant capacity for improving their quality of life.Article Consistent Estimation of Complete Neuronal Connectivity in Large Neuronal Populations Using Sparse Shotgun Neuronal Activity Sampling(Springer, 2016) Mishchenko, YuriyWe investigate the properties of recently proposed shotgun sampling approach for the common inputs problem in the functional estimation of neuronal connectivity. We study the asymptotic correctness, the speed of convergence, and the data size requirements of such an approach. We show that the shotgun approach can be expected to allow the inference of complete connectivity matrix in large neuronal populations under some rather general conditions. However, we find that the posterior error of the shotgun connectivity estimator grows quickly with the size of unobserved neuronal populations, the square of average connectivity strength, and the square of observation sparseness. This implies that the shotgun connectivity estimation will require significantly larger amounts of neuronal activity data whenever the number of neurons in observed neuronal populations remains small. We present a numerical approach for solving the shotgun estimation problem in general settings and use it to demonstrate the shotgun connectivity inference in the examples of simulated synfire and weakly coupled cortical neuronal networks.Research Project Daha Verimli Noninvaziv Beyin Makine Arayüzlerinin Geliştirilmesi(2018) Kaya, Murat; Yanar, Hilmi; Özbay, Erkan; Sürmeli, Umut; Sağlam, Emre; Mishchenko, Yuriy; Yılmaz, ZehraSon yıllarda nöral aktivite görüntüleme ve analiz tekniklerinin hızlı gelişimi, beyinde bilginin nasıl işlendiğinin temellerini anlamamıza yardımcı olmuştur. Aynı zamanda, beyinde bilgi işleyişi hakkında bilgi elde eden yeni yaklaşımlar ve bunlara bağlı gelişmeler birçok tibbi nörolojik durumların yeni tedavisine yol açmıştır. Beyin-Makine veya Beyin-Bilgisayar Arayüzleri (BMA/BBA) böyle gelişmelerden bir tanedir. BMA, nörobilim, istatistik ve sayısal yöntemler ile birlikte ortaya çıkan araştırma alanı olup, insanlarla iletişim ve kontrol için beyindeki nöral aktiviteyi doğrudan kullanmaya hedeflenmektedir. BMA, son 15 yılda hızlı ilerleyip, sanal ve gerçek durumda robotik maniplatörün kontrolü gibi imkanları felçli insanlara sağlamaktadır. Noninvaziv BMA da görüntüleme için kafatası-dışı beyin aktivite görüntüleme teknikleri, başlıca elektroensefalografi (EEG), kullanır ve son yıllarda çok hızlı gelişmiştir. Bu projede, EEG beyin görüntüleme tekniği kullanılarak orijinal bir BMA sistemi geliştirilmekte ve bu sistem kapsamında yeni EEG veri analiz ve modelleme teknikleri araştırılmaktadır. Proje süresince, tam bir EEG BMA sistemi geliştirilmekte, EEG veri analizi için yeni yöntemler araştırılmakta, EEG veri konusunda EEG BMA ile ilgili temel yeni bilgiler toplanmakta ve EEG verileri için yeni istatistiksel veri modelleme yöntemi incelenmektedir.Article Elektroensefalografi Beyin-makine Arayüzlerin Modellemesi(2020) Mishchenko, Yuriy; Yıldız, ZehraNörobilimdeki nöral aktivite görüntüleme ve analiz tekniklerinin son yıllarda hızlı gelişimi, bilginin beyindeki sinir ağlarında nasıl işlendiğini anlamamıza yardımcı olmuştur. Sinir ağlarının düzeni ve işleyişi hakkında elde edilen yeni yaklaşımlar ve bunlara bağlı gelişmeler sayesinde çözümlenmesi imkansız gibi görünen tıbbi nörolojik durumlar tedavi edilebilecek, motor ve iletişim yetersizliği olan binlerce insan için hayat kalitesini iyileştirebilecek radikal yeni iletişim sistemleri ve tıbbi protezler yapılabilecektir. Beyin-Makine ya da Beyin-Bilgisayar Arayüzleri (BBA) son 10-15 yılda hızlı ilerlemeler kaydeden yeni bir araştırma alanıdır. Noninvaziv elektroensefalografi (EEG) görüntüleme, fonksiyonel manyetik rezonans görüntüleme, deneklerin görsel hafızaları üzerinde başarılı sonuçlar verebileceği görülmüştür. Bu çalışmada, EEG beyin aktivite görüntüleme tekniğini kullanan BBA sistemlerinin pratik uygulamaları ve etkinliğini artırmak için için verimli istatistiksel nöral veri analiz teknikleri ve BBA deneysel tasarımları incelenmiştir. İstatistiksel nöral aktivite dinamik modelleri, temel nörobilimde beyindeki nöral aktivite analizi ve yorumlanmasında son yıllarda başarılı olduğundan bu çalışmada EEG BBA nöral aktivite verilerin kullanan dinamik modelleme üzerinde yoğunlaşılmıştır. Bu çalışma hem uluslararası alanda hem de Türkiye’de kullanılan sağlık, sivil ve askeri uygulamalar ile yürüme protezleri, karar verme sistemleri veya yarı otomatik robot ve makine sistemleri gibi cihazların kontrolüne yardımcı veya yüksek seviye kontrolü sağlayan komple BBA çözümlerinin Türkiye’de geliştirilmesine katkıda bulunacaktır.Data Paper Citation - WoS: 112Citation - Scopus: 129A Large Electroencephalographic Motor Imagery Dataset for Electroencephalographic Brain Computer Interfaces(Nature Publishing Group, 2018) Kaya, Murat; Binli, Mustafa Kemal; Ozbay, Erkan; Yanar, Hilmi; Mishchenko, YuriyRecent advancements in brain computer interfaces (BCI) have demonstrated control of robotic systems by mental processes alone. Together with invasive BCI, electroencephalographic (EEG) BCI represent an important direction in the development of BCI systems. In the context of EEG BCI, the processing of EEG data is the key challenge. Unfortunately, advances in that direction have been complicated by a lack of large and uniform datasets that could be used to design and evaluate different data processing approaches. In this work, we release a large set of EEG BCI data collected during the development of a slow cortical potentials-based EEG BCI. The dataset contains 60 h of EEG recordings, 13 participants, 75 recording sessions, 201 individual EEG BCI interaction session-segments, and over 60 000 examples of motor imageries in 4 interaction paradigms. The current dataset presents one of the largest EEG BCI datasets publically available to date.Article Citation - Scopus: 1Dark Matter Phenomenology of High-Speed Galaxy Cluster Collisions(Springer, 2017) Mishchenko, Yuriy; Ji, Chueng-RyongWe perform a general computational analysis of possible post-collision mass distributions in high-speed galaxy cluster collisions in the presence of self-interacting dark matter. Using this analysis, we show that astrophysically weakly self-interacting dark matter can impart subtle yet measurable features in the mass distributions of colliding galaxy clusters even without significant disruptions to the dark matter halos of the colliding galaxy clusters themselves. Most profound such evidence is found to reside in the tails of dark matter halos' distributions, in the space between the colliding galaxy clusters. Such features appear in our simulations as shells of scattered dark matter expanding in alignment with the outgoing original galaxy clusters, contributing significant densities to projected mass distributions at large distances from collision centers and large scattering angles of up to 90 degrees. Our simulations indicate that as much as 20% of the total collision's mass may be deposited into such structures without noticeable disruptions to the main galaxy clusters. Such structures at large scattering angles are forbidden in purely gravitational high-speed galaxy cluster collisions. Convincing identification of such structures in real colliding galaxy clusters would be a clear indication of the self-interacting nature of dark matter. Our findings may offer an explanation for the ring-like dark matter feature recently identified in the long-range reconstructions of the mass distribution of the colliding galaxy cluster CL0024+017.