Controller based on deep neural networks for SSVEP signal classification for control of Quadcopter-BMI System

سال انتشار: 1402
محل انتشار: هشتمین کنفرانس بین المللی پژوهش در علوم و مهندسی و پنجمین کنگره بین المللی عمران، معماری و شهرسازی آسیا
کد COI اختصاصی: ICRSIE08_117
زبان مقاله: انگلیسی
تعداد مشاهده: 76

نویسندگان

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

چکیده

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

Deep Learning, EEG processing, Quadqopter Control, SSVEP, Brain Computer Interface

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