Classification of EEG signals related to real and imagery knee movements using deep learning for brain computer interfaces.

Journal: Technology and health care : official journal of the European Society for Engineering and Medicine

Published Date: Jan 1, 2023

Abstract

BACKGROUND: Non-invasive Brain-Computer Interface (BCI) uses an electroencephalogram (EEG) to obtain information on brain neural activity. Because EEG can be contaminated by various artifacts during the collection process, it has primarily evolved into motor imagery (MI) with a low risk of contamination. However, MI has a disadvantage in that accurate data is difficult to obtain.

Authors

Yeji Lee

Department of Biomedical Engineering, Konyang University, Daejeon, Korea.
Hyun Ju Lee

Department of Pediatrics, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea.
Ki Sik Tae

Department of Biomedical Engineering, Konyang University, Daejeon, Korea.

Keywords

Algorithms Brain-Computer Interfaces Deep Learning Electroencephalography Humans Imagination Movement Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (36617798)

Classification of EEG signals related to real and imagery knee movements using deep learning for brain computer interfaces.

Abstract

Authors

Keywords

External Resources

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