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Open AccessReview

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

1
Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK
2
School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510665, China
3
The Guangzhou Key Laboratory of Digital Content Processing and Security Technologies, Guangzhou 510665, China
4
Department of Computer Systems and Telematics Engineering, Universidad de Extremadura, 06007 Badajoz, Spain
5
School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(6), 1423; https://doi.org/10.3390/s19061423
Received: 30 January 2019 / Revised: 10 March 2019 / Accepted: 19 March 2019 / Published: 22 March 2019
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. View Full-Text
Keywords: brain-computer interface (BCI); electroencephalography (EEG); motor-imagery (MI) brain-computer interface (BCI); electroencephalography (EEG); motor-imagery (MI)
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MDPI and ACS Style

Padfield, N.; Zabalza, J.; Zhao, H.; Masero, V.; Ren, J. EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges. Sensors 2019, 19, 1423. https://doi.org/10.3390/s19061423

AMA Style

Padfield N, Zabalza J, Zhao H, Masero V, Ren J. EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges. Sensors. 2019; 19(6):1423. https://doi.org/10.3390/s19061423

Chicago/Turabian Style

Padfield, Natasha; Zabalza, Jaime; Zhao, Huimin; Masero, Valentin; Ren, Jinchang. 2019. "EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges" Sensors 19, no. 6: 1423. https://doi.org/10.3390/s19061423

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