TY - JOUR
T1 - EEG-based brain-computer interfaces using motor-imagery
T2 - techniques and challenges
AU - Padfield, Natasha
AU - Zabalza, Jaime
AU - Zhao, Huimin
AU - Vargas, Valentin Masero
AU - Ren, Jinchang
PY - 2019/3/22
Y1 - 2019/3/22
N2 - 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.
AB - 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.
KW - brain-computer interfaces
KW - electroencephalography
KW - motor-imagery
UR - https://www.mdpi.com/journal/sensors
U2 - 10.3390/s19061423
DO - 10.3390/s19061423
M3 - Review article
VL - 19
JO - Sensors
JF - Sensors
SN - 1424-8220
IS - 6
M1 - 1423
ER -