Advances in Hybrid Brain-Computer Interfaces: Principles, Design, and Applications

Overview

Journal Comput Intell Neurosci

Specialty Biology

Date 2019 Nov 6

PMID 31687006

Citations 13

Authors

Zina Li

Shuqing Zhang

Jiahui Pan

Affiliations

Soon will be listed here.

Abstract

Conventional brain-computer interface (BCI) systems have been facing two fundamental challenges: the lack of high detection performance and the control command problem. To this end, the researchers have proposed a hybrid brain-computer interface (hBCI) to address these challenges. This paper mainly discusses the research progress of hBCI and reviews three types of hBCI, namely, hBCI based on multiple brain models, multisensory hBCI, and hBCI based on multimodal signals. By analyzing the general principles, paradigm designs, experimental results, advantages, and applications of the latest hBCI system, we found that using hBCI technology can improve the detection performance of BCI and achieve multidegree/multifunctional control, which is significantly superior to single-mode BCIs.

Citing Articles

A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features.

Cao Y, Gao S, Yu H, Zhao Z, Zang D, Wang C Front Physiol. 2024; 15:1487809.

PMID: 39703669 PMC: 11655504. DOI: 10.3389/fphys.2024.1487809.

Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques.

Chen D, Zhao Z, Zhang S, Chen S, Wu X, Shi J Transl Stroke Res. 2024; .

PMID: 38558011 DOI: 10.1007/s12975-024-01244-x.

Brain-computer interface paradigms and neural coding.

Tai P, Ding P, Wang F, Gong A, Li T, Zhao L Front Neurosci. 2024; 17:1345961.

PMID: 38287988 PMC: 10822902. DOI: 10.3389/fnins.2023.1345961.

Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios.

Vecchiato G Front Neuroergon. 2024; 2:784827.

PMID: 38235223 PMC: 10790909. DOI: 10.3389/fnrgo.2021.784827.

EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs.

Rosanne O, Alves de Oliveira A, Falk T Sensors (Basel). 2023; 23(23).

PMID: 38067725 PMC: 10708818. DOI: 10.3390/s23239352.

References

Chiarelli A, Croce P, Merla A, Zappasodi F . Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification. J Neural Eng. 2018; 15(3):036028. DOI: 10.1088/1741-2552/aaaf82. View

Halme H, Parkkonen L . Across-subject offline decoding of motor imagery from MEG and EEG. Sci Rep. 2018; 8(1):10087. PMC: 6031658. DOI: 10.1038/s41598-018-28295-z. View

Khan M, Hong M, Hong K . Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface. Front Hum Neurosci. 2014; 8:244. PMC: 4009438. DOI: 10.3389/fnhum.2014.00244. View

Shin J, von Luhmann A, Blankertz B, Kim D, Jeong J, Hwang H . Open Access Dataset for EEG+NIRS Single-Trial Classification. IEEE Trans Neural Syst Rehabil Eng. 2016; 25(10):1735-1745. DOI: 10.1109/TNSRE.2016.2628057. View

Long J, Li Y, Wang H, Yu T, Pan J, Li F . A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehabil Eng. 2012; 20(5):720-9. DOI: 10.1109/TNSRE.2012.2197221. View

Choi B, Jo S . A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition. PLoS One. 2013; 8(9):e74583. PMC: 3762758. DOI: 10.1371/journal.pone.0074583. View

Buccino A, Keles H, Omurtag A . Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks. PLoS One. 2016; 11(1):e0146610. PMC: 4701662. DOI: 10.1371/journal.pone.0146610. View

An X, Hohne J, Ming D, Blankertz B . Exploring combinations of auditory and visual stimuli for gaze-independent brain-computer interfaces. PLoS One. 2014; 9(10):e111070. PMC: 4211702. DOI: 10.1371/journal.pone.0111070. View

Li Y, Pan J, Wang F, Yu Z . A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control. IEEE Trans Biomed Eng. 2013; 60(11):3156-66. DOI: 10.1109/TBME.2013.2270283. View

10.

Wang F, He Y, Pan J, Xie Q, Yu R, Zhang R . A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection. Sci Rep. 2015; 5:9962. PMC: 4485169. DOI: 10.1038/srep09962. View

11.

Rutkowski T, Mori H . Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users. J Neurosci Methods. 2014; 244:45-51. DOI: 10.1016/j.jneumeth.2014.04.010. View

12.

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

13.

Fazli S, Mehnert J, Steinbrink J, Curio G, Villringer A, Muller K . Enhanced performance by a hybrid NIRS-EEG brain computer interface. Neuroimage. 2011; 59(1):519-29. DOI: 10.1016/j.neuroimage.2011.07.084. View

14.

Li Y, Long J, Yu T, Yu Z, Wang C, Zhang H . An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythm and P300 potential. IEEE Trans Biomed Eng. 2010; 57(10):2495-505. DOI: 10.1109/TBME.2010.2055564. View

15.

Hong K, Khan M . Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review. Front Neurorobot. 2017; 11:35. PMC: 5522881. DOI: 10.3389/fnbot.2017.00035. View

16.

Shin J, Kwon J, Im C . A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State. Front Neuroinform. 2018; 12:5. PMC: 5829061. DOI: 10.3389/fninf.2018.00005. View

17.

Wang F, He Y, Qu J, Xie Q, Lin Q, Ni X . Enhancing clinical communication assessments using an audiovisual BCI for patients with disorders of consciousness. J Neural Eng. 2017; 14(4):046024. DOI: 10.1088/1741-2552/aa6c31. View

18.

Xu M, Qi H, Wan B, Yin T, Liu Z, Ming D . A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature. J Neural Eng. 2013; 10(2):026001. DOI: 10.1088/1741-2560/10/2/026001. View

19.

Wolpaw J, Birbaumer N, McFarland D, Pfurtscheller G, Vaughan T . Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002; 113(6):767-91. DOI: 10.1016/s1388-2457(02)00057-3. View

20.

Panicker R, Puthusserypady S, Sun Y . An asynchronous P300 BCI with SSVEP-based control state detection. IEEE Trans Biomed Eng. 2011; 58(6):1781-8. DOI: 10.1109/TBME.2011.2116018. View