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Review
. 2019 Mar 22;19(6):1423.
doi: 10.3390/s19061423.

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

Natasha Padfield  1 , Jaime Zabalza  2 , Huimin Zhao  3   4 , Valentin Masero  5 , Jinchang Ren  6   7
Affiliations
Review

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

Natasha Padfield et al. Sensors (Basel). .

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.

Keywords: brain-computer interface (BCI); electroencephalography (EEG); motor-imagery (MI).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A diagram showing the signal processing carried out in a typical MI EEG-based system.
Figure 2
Figure 2
A diagram summarizing some of the feature extraction, feature selection and classification techniques used in MI EEG-based BCIs.
Figure 3
Figure 3
A diagram of the feature extraction and feature selection process proposed in [3].
Figure 4
Figure 4
This figure includes information about the acquisition of data set number 4 in BNCI Horizon 2020 [117], where (a) shows the electrodes (C3, CZ, C4) placement on the head [118] and (b) shows the time scheme paradigm [118] followed during data acquisition.
Figure 5
Figure 5
A diagram of the methodology proposed by the University of Strathclyde team in the BDBC 2017 hosted in Glasgow, where different feature extraction techniques were compared under the same conditions.
Figure 6
Figure 6
Performance comparison among TM, SM, A-BP, S-BP and FFT feature extraction techniques evaluated under the same conditions, where (a) shows the classification accuracy (%) and (b) shows the approximated computation time (μs) required to extract the features.
Figure 7
Figure 7
This figure shows the hardware setup used for a low-cost MI-based EEG system e.g., in [133], [136] where (a) shows the 3D-printed prosthetic arm which was controlled and (b) shows the EEG headset used.
See this image and copyright information in PMC

References

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