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

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

Research output: Contribution to journalReview article

3 Citations (Scopus)

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.
LanguageEnglish
Article number1423
Number of pages36
JournalSensors
Volume19
Issue number6
DOIs
Publication statusPublished - 22 Mar 2019

Fingerprint

Brain-Computer Interfaces
electroencephalography
Brain computer interface
Imagery (Psychotherapy)
Electroencephalography
imagery
brain
Feature extraction
commercialization
limbs
pattern recognition
signal processing
Signal processing
Extremities
Technology

Keywords

  • brain-computer interfaces
  • electroencephalography
  • motor-imagery

Cite this

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title = "EEG-based brain-computer interfaces using motor-imagery: techniques and challenges",
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.",
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EEG-based brain-computer interfaces using motor-imagery : techniques and challenges. / Padfield, Natasha; Zabalza, Jaime; Zhao, Huimin; Vargas, Valentin Masero; Ren, Jinchang.

In: Sensors, Vol. 19, No. 6, 1423, 22.03.2019.

Research output: Contribution to journalReview article

TY - JOUR

T1 - EEG-based brain-computer interfaces using motor-imagery

T2 - Sensors

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

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M1 - 1423

ER -