Találati lista | Tudóstér

001-es BibID:	BIBFORM126075
035-os BibID:	(WOS)001332183600001
Első szerző:	Xie, Yu
Cím:	Enhancing Motor Imagery Classification in Brain-Computer Interfaces Using Deep Learning and Continuous Wavelet Transform / Yu Xie, Stefan Oniga
Dátum:	2024
ISSN:	2076-3417
Megjegyzések:	In brain-computerinterface (BCI) systems, motorimagery(MI)electroencephalogram(EEG) is widely used to interpret the human brain. However, MI classification is challenging due to weak signals and a lack of high-quality data. While deep learning (DL) methods have shown significant success in pattern recognition, their application to MI-based BCI systems remains limited. To address these challenges, we propose a novel deep learning algorithm that leverages EEG signal features through a two-branch parallel convolutional neural network (CNN). Our approach incorporates different input signals, such as continuous wavelet transform, short-time Fourier transform, and commonspatial patterns, and employs various classifiers, including support vector machines and decision trees, to enhance system performance. We evaluate our algorithm using the BCI Competition IV dataset 2B, comparing it with other state-of-the-art methods. Our results demonstrate that the proposed method excels in classification accuracy, offering improvements for MI-based BCI systems.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk EEG signal analysis continuous wavelet transform convolutional neural networks feature extraction
Megjelenés:	Applied Sciences-Basel. - 14 : 19 (2024), p. 1-16. -
További szerzők:	Oniga István László (1960-) (villamosmérnök)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: