Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/2813
Title: | Patient-Specific Imaginary Motor Movement Classification of Eeg Signals and Control of Robotic Arm | Authors: | Devecioglu, Ozer Can Yaman, Burak Mesekoparan, Ozle Cakir, Can İnce, Türker |
Keywords: | Deep Multilayer Perceptron Biomedical Signal Processing EEG Signals Imaginary Motor Movement |
Publisher: | IEEE | Abstract: | This paper presents development of a robotic arm that can mimic the mobility of human arm with the recorded EEG (electroencephalogram) signals from the brain. This is a project designed for people who have lost the ability to control their arms. The robot arm will work with the signals that will be produced by the EEG instrument simultaneously when the user thinks that he or she will perform certain movements. These movements can be classified using Deep Multilayer Perceptron (Deep MLP) classifier designed in Python with Keras library. Deep MLP network is trained using Backpropagation algorithm for each patient by using own data. Experimental results demonstrate the proposed method can classify different imaginary movements with high accuracy. | Description: | International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) / International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) -- AUG 27-29, 2019 -- Bahcesehir Univ, Istanbul, TURKEY | URI: | https://hdl.handle.net/20.500.14365/2813 | ISBN: | 978-1-5386-7687-5 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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