Controller based on deep neural networks for SSVEP signal classification for control of Quadcopter-BMI System
محل انتشار: هشتمین کنفرانس بین المللی پژوهش در علوم و مهندسی و پنجمین کنگره بین المللی عمران، معماری و شهرسازی آسیا
سال انتشار: 1402
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 38
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شناسه ملی سند علمی:
ICRSIE08_117
تاریخ نمایه سازی: 18 فروردین 1403
چکیده مقاله:
Quadcopters, typically known as drones, are being used in an increasing range of scenarios such as unmanned aerial vehicles. The goal of this research is to use electroencephalography (EEG) to establish a method for controlling drones using a brain–machine interface system based on the steady-state visual-evoked potential (SSVEP). To reduce the load on participants during a long-time usage, such a system must be simplified. The proposed method is, therefore, limited to one EEG channel. Drones can exhibit five types of movement: taking off (rising), moving forward, turning right, turning left, and landing. Participants are therefore presented with five multiflickers simultaneously. However, concerns arise over the effect on classification accuracy with using only one channel of the SSVEP. We, therefore, evaluated the classification accuracy using long-short-term memory, which is a method of deep learning that has garnered significant attention. After conducting an experiment with four healthy men, the results indicated a high accuracy of ۹۶% on average. A second experiment was conducted in which the three participants flew actual drones in a series of movements consisting of taking off, moving forward, and landing. We subsequently compared the accuracy of those movements and the flight times.
کلیدواژه ها:
نویسندگان
Saied Piri
Research Center for Computational Cognitive Neuroscience, System & Cybernetic Laboratory, Imam Reza International University, Mashhad, Iran
Arefeh Dinarvand
UAST-University of Applied Science and Technology X-IBM Institute, Tehran, Iran
Kazem Sohrabi
Bachelor of Aerospace Engineering majoring in air structures, Shahid Sattari Aeronautical University, Tehran, Iran