Automatic detection of cursor movements from the EEG signals via deep learning approach
Yükleniyor...
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE-Institute of Electrical Electronics Engineers INC.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The classification of motor imagery (MI) tasks is one of the key objectives of EEC-based brain-computer interface (BC!) systems. To ensure successful classification performance to BCI systems, researchers endeavor to extract appropriate features. However, these challenges are based on the conventional method. In this study, EEG signals related to MI tasks are classified using the convolutional neural network (CNN), which does not need a separate feature extraction. EEG records, which are generally evaluated as one-dimensional in machine learning problems, were taken into consideration as the image representation by using a novel method. The datasets were taken from a healthy subject. The subject was asked to move a cursor up and down on a computer screen, while his cortical potentials were taken. The EEG signals recorded over the 3.5-second time interval were evaluated for both the whole time and sub time intervals. Thus, the most effective time interval that has distinguishing features for EEG recordings related to different cursor movements was tried to be determined as well. As a result, it has been shown that the proposed model based on deep learning approach can successfully classify EEG signals related to cursor movements.
Açıklama
WOS:000629055500064
Anahtar Kelimeler
EEG, Deep learning, Convolutional Neural Networks, Classification
Kaynak
5th International Conference on Computer Science and Engineering (UBMK)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Polat, H. ve Özerdem, M.S. (2020, Sept 9-11). Automatic detection of cursor movements from the EEG signals via deep learning approach. [Proceedings Paper]. 5th International Conference on Computer Science and Engineering (UBMK). New York: IEEE. (pp. 327-332)
