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

Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK
School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510665, China
The Guangzhou Key Laboratory of Digital Content Processing and Security Technologies, Guangzhou 510665, China
Department of Computer Systems and Telematics Engineering, Universidad de Extremadura, 06007 Badajoz, Spain
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;
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.

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.

Chicago/Turabian Style

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

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