Ayluçtarhan, Gülşen

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https://gcris.yasar.edu.tr/handle/123456789/12809
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Araş.Gör.
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01. Yaşar Üniversitesi
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CCV-7798-2022

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    Master Thesis
    Eeg tabanlı robotik kol kontrolü için makine öğrenme algoritmalarının uygulanması
    (2019) Ayluçtarhan, Gülşen; Zincir, İbrahim
    Electroencephalography (EEG) analysis has been an important subject of several studies like neuroscience, medical diagnosis and rehabilitation engineering. EEG is widely used with brain-computer interface (BCI) systems because of its ability to use brain signals not muscles to control an external BCI prosthetic device. With the development of technology, it became possible to use large EEG datasets and BCI method to extract an understandable information. In this present work, EEG-based BCI system is used by making participants perform a series of grasping and lifting hand movements. Dataset which consists of EEG and EMG information has been implemented via 15 machine learning algorithms as multiclass classification. The best results came from IB1 algorithm. But, random forest, bagging and classification via regression algorithms also have promising outcomes. Hence, this study successfully proved that it is possible to help patients with no hand function to gain control.