Hybrid Brain Computer Interface via Bayesian integration of EEG and eye gaze

Xujiong Dong; Haofei Wang; Zhaokang Chen; Bertram E. Shi

doi:10.1109/NER.2015.7146582

Hybrid Brain Computer Interface via Bayesian integration of EEG and eye gaze

Xujiong Dong, Haofei Wang, Zhaokang Chen, Bertram E. Shi

HKUST Fok Ying Tung Graduate School

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

20 Citations (Scopus)

Abstract

We describe a hybrid brain computer interface that integrates information from a four-class motor imagery based EEG classifier with information about gaze trajectories from an eye tracker. The novel aspect of this system is that no explicit gaze behavior is required of the user. Rather, the natural gaze behavior of the user integrated probabilistically to smooth the noisy classification results from the motor imagery based EEG. The goal is to provide for a more natural interaction with the BCI system than if gaze were used as an explicit command signal, as is commonly done. Our results on a 2D cursor control task show that integration of gaze information significantly improves task completion accuracy and reduces task completion time. In particular, our system achieves over 80% target completion accuracy on a cursor control task requiring guidance to one of 12 targets.

Original language	English
Title of host publication	2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
Publisher	IEEE Computer Society
Pages	150-153
Number of pages	4
ISBN (Electronic)	9781467363891
DOIs	https://doi.org/10.1109/NER.2015.7146582
Publication status	Published - 1 Jul 2015
Event	7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 - Montpellier, France Duration: 22 Apr 2015 → 24 Apr 2015

Publication series

Name	International IEEE/EMBS Conference on Neural Engineering, NER
Volume	2015-July
ISSN (Print)	1948-3546
ISSN (Electronic)	1948-3554

Conference

Conference	7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
Country/Territory	France
City	Montpellier
Period	22/04/15 → 24/04/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

Hybrid Brain Computer Interface (BCI)
assistive technology
gaze control
human computer interaction

Access to Document

10.1109/NER.2015.7146582

Cite this

Dong, X., Wang, H., Chen, Z., & Shi, B. E. (2015). Hybrid Brain Computer Interface via Bayesian integration of EEG and eye gaze. In 2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 (pp. 150-153). Article 7146582 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/NER.2015.7146582

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title = "Hybrid Brain Computer Interface via Bayesian integration of EEG and eye gaze",

abstract = "We describe a hybrid brain computer interface that integrates information from a four-class motor imagery based EEG classifier with information about gaze trajectories from an eye tracker. The novel aspect of this system is that no explicit gaze behavior is required of the user. Rather, the natural gaze behavior of the user integrated probabilistically to smooth the noisy classification results from the motor imagery based EEG. The goal is to provide for a more natural interaction with the BCI system than if gaze were used as an explicit command signal, as is commonly done. Our results on a 2D cursor control task show that integration of gaze information significantly improves task completion accuracy and reduces task completion time. In particular, our system achieves over 80\% target completion accuracy on a cursor control task requiring guidance to one of 12 targets.",

keywords = "Hybrid Brain Computer Interface (BCI), assistive technology, gaze control, human computer interaction",

author = "Xujiong Dong and Haofei Wang and Zhaokang Chen and Shi, \{Bertram E.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 ; Conference date: 22-04-2015 Through 24-04-2015",

year = "2015",

month = jul,

day = "1",

doi = "10.1109/NER.2015.7146582",

language = "English",

series = "International IEEE/EMBS Conference on Neural Engineering, NER",

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Dong, X, Wang, H, Chen, Z & Shi, BE 2015, Hybrid Brain Computer Interface via Bayesian integration of EEG and eye gaze. in 2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015., 7146582, International IEEE/EMBS Conference on Neural Engineering, NER, vol. 2015-July, IEEE Computer Society, pp. 150-153, 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015, Montpellier, France, 22/04/15. https://doi.org/10.1109/NER.2015.7146582

Hybrid Brain Computer Interface via Bayesian integration of EEG and eye gaze. / Dong, Xujiong; Wang, Haofei; Chen, Zhaokang et al.
2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015. IEEE Computer Society, 2015. p. 150-153 7146582 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2015-July).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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AU - Dong, Xujiong

AU - Wang, Haofei

AU - Chen, Zhaokang

AU - Shi, Bertram E.

PY - 2015/7/1

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N2 - We describe a hybrid brain computer interface that integrates information from a four-class motor imagery based EEG classifier with information about gaze trajectories from an eye tracker. The novel aspect of this system is that no explicit gaze behavior is required of the user. Rather, the natural gaze behavior of the user integrated probabilistically to smooth the noisy classification results from the motor imagery based EEG. The goal is to provide for a more natural interaction with the BCI system than if gaze were used as an explicit command signal, as is commonly done. Our results on a 2D cursor control task show that integration of gaze information significantly improves task completion accuracy and reduces task completion time. In particular, our system achieves over 80% target completion accuracy on a cursor control task requiring guidance to one of 12 targets.

AB - We describe a hybrid brain computer interface that integrates information from a four-class motor imagery based EEG classifier with information about gaze trajectories from an eye tracker. The novel aspect of this system is that no explicit gaze behavior is required of the user. Rather, the natural gaze behavior of the user integrated probabilistically to smooth the noisy classification results from the motor imagery based EEG. The goal is to provide for a more natural interaction with the BCI system than if gaze were used as an explicit command signal, as is commonly done. Our results on a 2D cursor control task show that integration of gaze information significantly improves task completion accuracy and reduces task completion time. In particular, our system achieves over 80% target completion accuracy on a cursor control task requiring guidance to one of 12 targets.

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KW - assistive technology

KW - gaze control

KW - human computer interaction

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BT - 2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015

PB - IEEE Computer Society

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Y2 - 22 April 2015 through 24 April 2015

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Hybrid Brain Computer Interface via Bayesian integration of EEG and eye gaze

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