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

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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.

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Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials

L.A. Farwell, E Donchin (1988)

This paper describes the development and testing of a system whereby one can communicate through a computer by using the P300 component of the event-related brain potential (ERP). Such a system may be used as a communication aid by individuals who cannot use any motor system for communication (e.g., 'locked-in' patients). The 26 letters of the alphabet, together with several other symbols and commands, are displayed on a computer screen which serves as the keyboard or prosthetic device. The subject focuses attention successively on the characters he wishes to communicate. The computer detects the chosen character on-line and in real time. This detection is achieved by repeatedly flashing rows and columns of the matrix. When the elements containing the chosen character are flashed, a P300 is elicited, and it is this P300 that is detected by the computer. We report an analysis of the operating characteristics of the system when used with normal volunteers, who took part in 2 experimental sessions. In the first session (the pilot study/training session) subjects attempted to spell a word and convey it to a voice synthesizer for production. In the second session (the analysis of the operating characteristics of the system) subjects were required simply to attend to individual letters of a word for a specific number of trials while data were recorded for off-line analysis. The analyses suggest that this communication channel can be operated accurately at the rate of 0.20 bits/sec. In other words, under the conditions we used, subjects can communicate 12.0 bits, or 2.3 characters, per min.

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Enhanced performance by a hybrid NIRS-EEG brain computer interface.

Siamac Fazli, Jan Mehnert, Jens Steinbrink … (2012)

Noninvasive Brain Computer Interfaces (BCI) have been promoted to be used for neuroprosthetics. However, reports on applications with electroencephalography (EEG) show a demand for a better accuracy and stability. Here we investigate whether near-infrared spectroscopy (NIRS) can be used to enhance the EEG approach. In our study both methods were applied simultaneously in a real-time Sensory Motor Rhythm (SMR)-based BCI paradigm, involving executed movements as well as motor imagery. We tested how the classification of NIRS data can complement ongoing real-time EEG classification. Our results show that simultaneous measurements of NIRS and EEG can significantly improve the classification accuracy of motor imagery in over 90% of considered subjects and increases performance by 5% on average (p<0:01). However, the long time delay of the hemodynamic response may hinder an overall increase of bit-rates. Furthermore we find that EEG and NIRS complement each other in terms of information content and are thus a viable multimodal imaging technique, suitable for BCI. Copyright © 2011 Elsevier Inc. All rights reserved.

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The Hybrid BCI

Gert Pfurtscheller, Brendan Allison, Clemens Brunner … (2010)

Nowadays, everybody knows what a hybrid car is. A hybrid car normally has two engines to enhance energy efficiency and reduce CO2 output. Similarly, a hybrid brain-computer interface (BCI) is composed of two BCIs, or at least one BCI and another system. A hybrid BCI, like any BCI, must fulfill the following four criteria: (i) the device must rely on signals recorded directly from the brain; (ii) there must be at least one recordable brain signal that the user can intentionally modulate to effect goal-directed behaviour; (iii) real time processing; and (iv) the user must obtain feedback. This paper introduces hybrid BCIs that have already been published or are in development. We also introduce concepts for future work. We describe BCIs that classify two EEG patterns: one is the event-related (de)synchronisation (ERD, ERS) of sensorimotor rhythms, and the other is the steady-state visual evoked potential (SSVEP). Hybrid BCIs can either process their inputs simultaneously, or operate two systems sequentially, where the first system can act as a “brain switch”. For example, we describe a hybrid BCI that simultaneously combines ERD and SSVEP BCIs. We also describe a sequential hybrid BCI, in which subjects could use a brain switch to control an SSVEP-based hand orthosis. Subjects who used this hybrid BCI exhibited about half the false positives encountered while using the SSVEP BCI alone. A brain switch can also rely on hemodynamic changes measured through near-infrared spectroscopy (NIRS). Hybrid BCIs can also use one brain signal and a different type of input. This additional input can be an electrophysiological signal such as the heart rate, or a signal from an external device such as an eye tracking system.

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Author and article information

Contributors

Jiahui Pan:

ORCID: https://orcid.org/0000-0002-7576-6743

Journal

Journal ID (nlm-ta): Comput Intell Neurosci

Journal ID (iso-abbrev): Comput Intell Neurosci

Journal ID (publisher-id): CIN

Title: Computational Intelligence and Neuroscience

Publisher: Hindawi

ISSN (Print): 1687-5265

ISSN (Electronic): 1687-5273

Publication date Collection: 2019

Publication date (Electronic): 8 October 2019

Volume: 2019

Electronic Location Identifier: 3807670

Affiliations

South China Normal University, Guangzhou 510631, China

Author notes

Guest Editor: Hyun J. Baek

Author information

Jiahui Pan https://orcid.org/0000-0002-7576-6743

Article

DOI: 10.1155/2019/3807670

PMC ID: 6800963

PubMed ID: 31687006

SO-VID: d5979411-659a-4089-8737-29b6a5b50fda

License:

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 20 June 2019

Date revision received : 9 September 2019

Date accepted : 17 September 2019

Funding

Funded by: National Natural Science Foundation of China

Award ID: 61876067

Funded by: Pearl River S and T Nova Program of Guangzhou

Award ID: 201710010038

Funded by: Natural Science Foundation of Guangdong Province

Award ID: 2014A030310244

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

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Most cited references 51

Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials

Enhanced performance by a hybrid NIRS-EEG brain computer interface.

The Hybrid BCI

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