Motor Imagery Signal Classification Using Constant-Q Transform for BCI Applications
Küçük Resim Yok
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
European Signal Processing Conference, EUSIPCO
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Electroencephalography (EEG) signals have been using for brain-computer interface applications for the last two decades. Motor imagery (MI) signals are one of the EEG signal types formed by imagining a limb's movement. Recently with the help of deep neural networks (DNN) for classifying MI signals using time-frequency (TF) features, considerable performance improvement has been reported. This paper proposes using a well-known TF representation technique called Constant-Q Transform (CQT) for the MI signal classification. Experiments conducted on BCI IV 2b dataset with DNN classifier using CQT spectrogram show that CQT outperforms traditional short-time Fourier transform (STFT) representation.
Açıklama
Anahtar Kelimeler
Brain-computer interface, Constant-Q transform, Electroencephalography, Motor imagery
Kaynak
29th European Signal Processing Conference, EUSIPCO 2021
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
2021
