Browsing by Author "Kaya, Murat"

Now showing 1 - 4 of 4

Daha Verimli Noninvaziv Beyin Makine Arayüzlerinin Geliştirilmesi
(2018) Kaya, Murat; Yanar, Hilmi; Özbay, Erkan; Sürmeli, Umut; Sağlam, Emre; Mishchenko, Yuriy; Yılmaz, Zehra
Son 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.
Citation - WoS: 30
Citation - Scopus: 39
Developing 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, Hilmi
Objective: 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.
Citation - WoS: 73
Citation - Scopus: 103
Distinguishing 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, Yuriy
Recent 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.
Citation - WoS: 112
Citation - Scopus: 129
A Large Electroencephalographic Motor Imagery Dataset for Electroencephalographic Brain Computer Interfaces
(Nature Publishing Group, 2018) Kaya, Murat; Binli, Mustafa Kemal; Ozbay, Erkan; Yanar, Hilmi; Mishchenko, Yuriy
Recent 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.