EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.

Padfield, Natasha; Zabalza, Jaime; Zhao, Huimin; Masero, Valentin; Ren, Jinchang

Abstract

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

Citation

PADFIELD, N., ZABALZA, J., ZHAO, H., MASERO, V. and REN, J. 2019. EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. Sensors [online], 19(6), article 1423. Available from: https://doi.org/10.3390/s19061423

• Journal Article Type: Article
• Acceptance Date: Mar 19, 2019
• Online Publication Date: Mar 22, 2019
• Publication Date: Mar 31, 2019
• Deposit Date: Apr 26, 2022
• Publicly Available Date: Apr 26, 2022
• Journal: Sensors
• Print ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 6
• Article Number: 1423
• DOI: https://doi.org/10.3390/s19061423
• Keywords: Brain-computer interface (BCI); Electroencephalography (EEG); Motor-imagery (MI)
• Public URL: https://rgu-repository.worktribe.com/output/1085674