Classification of Motor Imagery Signals by Convolutional Neural Network for BCI Applications
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Electroencephalography (EEG) signals have been using for clinical purposes for many years. However, studies on the use of EEG signals in brain computer interface (BBA) applications are increasing. It is possible to control machines using only mental activities, especially for patients with limited mobility. Motor imagery signals (MIS) which are formed as a result of the imagination of moving a limb are one of the most common signal used for this purpose. In this study, it is aimed to classify MIS signals with Convolutional Neural Network by using BCI-IV 2b dataset. As a result, higher (%75,7) performance was obtained with lower number of parameters compared to similar previous studies.
27th Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2019 -- Sivas Cumhuriyet Univ, Sivas, TURKEY
Motor imagery, brain computer interface, electroencephalography, deep learning
2019 27Th Signal Processing And Communications Applications Conference (Siu)
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