Browsing by Author "Ozbay, Erkan"

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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: 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.