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

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 6; p. 1423
• Main Authors: Padfield, Natasha, Zabalza, Jaime, Zhao, Huimin, Masero, Valentin, Ren, Jinchang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 22.03.2019; MDPI AG
• Subjects: Brain - physiology; brain-computer interface (BCI); Brain-Computer Interfaces; electroencephalography (EEG); Electroencephalography - methods; Evoked Potentials; Humans; motor-imagery (MI); Principal Component Analysis; Review; Signal Processing, Computer-Assisted; electroencephalography (EEG); motor-imagery (MI); brain-computer interface (BCI)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19061423

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.