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

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2019 Mar 27

PMID 30909489

Citations 83

Authors

Natasha Padfield

Jaime Zabalza

Huimin Zhao

Valentin Masero

Jinchang Ren

Affiliations

Soon will be listed here.

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.

Citing Articles

Paradigms and methods of noninvasive brain-computer interfaces in motor or communication assistance and rehabilitation: a systematic review.

Meng J, Wei Y, Mai X, Li S, Wang X, Luo R Med Biol Eng Comput. 2025; .

PMID: 40059266 DOI: 10.1007/s11517-025-03340-y.

MSEI-ENet: A Multi-Scale EEG-Inception Integrated Encoder Network for Motor Imagery EEG Decoding.

Wu P, Fei K, Chen B, Pan L Brain Sci. 2025; 15(2).

PMID: 40002462 PMC: 11853029. DOI: 10.3390/brainsci15020129.

Response coupling with an auxiliary neural signal for enhancing brain signal detection.

Gupta E, Sivakumar R Sci Rep. 2025; 15(1):6227.

PMID: 39979351 PMC: 11842634. DOI: 10.1038/s41598-025-87414-9.

Brain-Computer Interfaces for Upper Limb Motor Recovery after Stroke: Current Status and Development Prospects (Review).

Mokienko O, Lyukmanov R, Bobrov P, Suponeva N, Piradov M Sovrem Tekhnologii Med. 2025; 15(6):63-73.

PMID: 39944367 PMC: 11811833. DOI: 10.17691/stm2023.15.6.07.

Stochasticity as a solution for overfitting-A new model and comparative study on non-invasive EEG prospects.

Radwan Y, Ahmed Mohamed E, Metwalli D, Barakat M, Ahmed A, Kiroles A Front Hum Neurosci. 2025; 19:1484470.

PMID: 39925722 PMC: 11802819. DOI: 10.3389/fnhum.2025.1484470.

References

Yu T, Xiao J, Wang F, Zhang R, Gu Z, Cichocki A . Enhanced Motor Imagery Training Using a Hybrid BCI With Feedback. IEEE Trans Biomed Eng. 2015; 62(7):1706-17. DOI: 10.1109/TBME.2015.2402283. View

Athanasiou A, Xygonakis I, Pandria N, Kartsidis P, Arfaras G, Kavazidi K . Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms. Biomed Res Int. 2017; 2017:5708937. PMC: 5602625. DOI: 10.1155/2017/5708937. View

Scherer R, Faller J, Friedrich E, Opisso E, Costa U, Kubler A . Individually adapted imagery improves brain-computer interface performance in end-users with disability. PLoS One. 2015; 10(5):e0123727. PMC: 4436356. DOI: 10.1371/journal.pone.0123727. View

Nauck D, Kruse R . Obtaining interpretable fuzzy classification rules from medical data. Artif Intell Med. 1999; 16(2):149-69. DOI: 10.1016/s0933-3657(98)00070-0. View

Alam S, Bhuiyan M . Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE J Biomed Health Inform. 2013; 17(2):312-8. DOI: 10.1109/JBHI.2012.2237409. View

Marchesotti S, Bassolino M, Serino A, Bleuler H, Blanke O . Quantifying the role of motor imagery in brain-machine interfaces. Sci Rep. 2016; 6:24076. PMC: 4823701. DOI: 10.1038/srep24076. View

Dornhege G, Blankertz B, Krauledat M, Losch F, Curio G, Muller K . Combined optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE Trans Biomed Eng. 2006; 53(11):2274-81. DOI: 10.1109/TBME.2006.883649. View

Martisius I, Damasevicius R . A Prototype SSVEP Based Real Time BCI Gaming System. Comput Intell Neurosci. 2016; 2016:3861425. PMC: 4804071. DOI: 10.1155/2016/3861425. View

Koo B, Lee H, Nam Y, Kang H, Koh C, Shin H . A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery. J Neurosci Methods. 2014; 244:26-32. DOI: 10.1016/j.jneumeth.2014.04.016. View

10.

Thomas K, Guan C, Lau C, Vinod A, Ang K . A new discriminative common spatial pattern method for motor imagery brain-computer interfaces. IEEE Trans Biomed Eng. 2009; 56(11 Pt 2):2730-3. DOI: 10.1109/TBME.2009.2026181. View

11.

Nicolas-Alonso L, Gomez-Gil J . Brain computer interfaces, a review. Sensors (Basel). 2012; 12(2):1211-79. PMC: 3304110. DOI: 10.3390/s120201211. View

12.

Schreuder M, Hohne J, Blankertz B, Haufe S, Dickhaus T, Tangermann M . Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods. J Neural Eng. 2013; 10(3):036025. DOI: 10.1088/1741-2560/10/3/036025. View

13.

Lawhern V, Kerick S, Robbins K . Detecting alpha spindle events in EEG time series using adaptive autoregressive models. BMC Neurosci. 2013; 14:101. PMC: 3848457. DOI: 10.1186/1471-2202-14-101. View

14.

Qu H, Gotman J . A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use as a warning device. IEEE Trans Biomed Eng. 1997; 44(2):115-22. DOI: 10.1109/10.552241. View

15.

Bin G, Gao X, Wang Y, Li Y, Hong B, Gao S . A high-speed BCI based on code modulation VEP. J Neural Eng. 2011; 8(2):025015. DOI: 10.1088/1741-2560/8/2/025015. View

16.

Schlogl A, Lee F, Bischof H, Pfurtscheller G . Characterization of four-class motor imagery EEG data for the BCI-competition 2005. J Neural Eng. 2005; 2(4):L14-22. DOI: 10.1088/1741-2560/2/4/L02. View

17.

Thibault R, Lifshitz M, Jones J, Raz A . Posture alters human resting-state. Cortex. 2014; 58:199-205. DOI: 10.1016/j.cortex.2014.06.014. View

18.

Krusienski D, McFarland D, Wolpaw J . An evaluation of autoregressive spectral estimation model order for brain-computer interface applications. Conf Proc IEEE Eng Med Biol Soc. 2007; 2006:1323-6. DOI: 10.1109/IEMBS.2006.259822. View

19.

Jurysta F, van de Borne P, Migeotte P, Dumont M, Lanquart J, Degaute J . A study of the dynamic interactions between sleep EEG and heart rate variability in healthy young men. Clin Neurophysiol. 2003; 114(11):2146-55. DOI: 10.1016/s1388-2457(03)00215-3. View

20.

Picton T . The P300 wave of the human event-related potential. J Clin Neurophysiol. 1992; 9(4):456-79. DOI: 10.1097/00004691-199210000-00002. View