A subject-independent brain-computer interface based on smoothed, second-order baselining.

B. Reuderink; Jason Farquhar; Mannes Poel; Antinus Nijholt

doi:10.1109/IEMBS.2011.6091139

A subject-independent brain-computer interface based on smoothed, second-order baselining.

B. Reuderink, Jason Farquhar, Mannes Poel, Antinus Nijholt

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

35 Citations (Scopus)

Abstract

A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Traditional approaches to BCIs require the user to train for weeks or even months to learn to control the BCI. In contrast, BCIs based on machine learning only require a calibration session of less than an hour before the system can be used, since the machine adapts to the user's existing brain signals. However, this calibration session has to be repeated before each use of the BCI due to inter-session variability, which makes using a BCI still a time-consuming and an error-prone enterprise. In this work, we present a second-order baselining procedure that reduces these variations, and enables the creation of a BCI that can be applied to new subjects without such a calibration session. The method was validated with a motor-imagery classification task performed by 109 subjects. Results showed that our subject-independent BCI without calibration performs as well as the popular common spatial patterns (CSP)-based BCI that does use a calibration session.

Original language	English
Title of host publication	2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	4600-4604
Number of pages	5
ISBN (Electronic)	978-1-4577-1589-1, 978-1-4244-4122-8
ISBN (Print)	978-1-4244-4121-1
DOIs	https://doi.org/10.1109/IEMBS.2011.6091139
Publication status	Published - 3 Sep 2011
Event	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011 - Boston Marriott Copley Place Hotel, Boston, United States Duration: 30 Aug 2011 → 3 Sep 2011 Conference number: 33

Publication series

Name	Proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher	IEEE
Volume	2011
ISSN (Print)	1557-170X
ISSN (Electronic)	1558-4615

Conference

Conference	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011
Abbreviated title	EMBC
Country	United States
City	Boston
Period	30/08/11 → 3/09/11

Keywords

Machine learning
Brain-Computer Interface (BCI)
Training
HMI-CI: Computational Intelligence
Calibration

Access to Document

10.1109/IEMBS.2011.6091139

Cite this

Reuderink, B., Farquhar, J., Poel, M., & Nijholt, A. (2011). A subject-independent brain-computer interface based on smoothed, second-order baselining. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 4600-4604). (Proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Vol. 2011). IEEE. https://doi.org/10.1109/IEMBS.2011.6091139

Reuderink, B. ; Farquhar, Jason ; Poel, Mannes ; Nijholt, Antinus. / A subject-independent brain-computer interface based on smoothed, second-order baselining. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway, NJ : IEEE, 2011. pp. 4600-4604 (Proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society).

@inproceedings{48727c34e1f943aa82927c0434cbb6ee,

title = "A subject-independent brain-computer interface based on smoothed, second-order baselining.",

abstract = "A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Traditional approaches to BCIs require the user to train for weeks or even months to learn to control the BCI. In contrast, BCIs based on machine learning only require a calibration session of less than an hour before the system can be used, since the machine adapts to the user's existing brain signals. However, this calibration session has to be repeated before each use of the BCI due to inter-session variability, which makes using a BCI still a time-consuming and an error-prone enterprise. In this work, we present a second-order baselining procedure that reduces these variations, and enables the creation of a BCI that can be applied to new subjects without such a calibration session. The method was validated with a motor-imagery classification task performed by 109 subjects. Results showed that our subject-independent BCI without calibration performs as well as the popular common spatial patterns (CSP)-based BCI that does use a calibration session.",

keywords = "Machine learning, Brain-Computer Interface (BCI), Training, HMI-CI: Computational Intelligence, Calibration",

author = "B. Reuderink and Jason Farquhar and Mannes Poel and Antinus Nijholt",

year = "2011",

month = sep,

day = "3",

doi = "10.1109/IEMBS.2011.6091139",

language = "English",

isbn = "978-1-4244-4121-1",

series = "Proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society",

publisher = "IEEE",

pages = "4600--4604",

booktitle = "2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society",

address = "United States",

note = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, EMBC ; Conference date: 30-08-2011 Through 03-09-2011",

}

Reuderink, B, Farquhar, J, Poel, M & Nijholt, A 2011, A subject-independent brain-computer interface based on smoothed, second-order baselining. in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2011, IEEE, Piscataway, NJ, pp. 4600-4604, 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, Boston, United States, 30/08/11. https://doi.org/10.1109/IEMBS.2011.6091139

A subject-independent brain-computer interface based on smoothed, second-order baselining. / Reuderink, B.; Farquhar, Jason; Poel, Mannes ; Nijholt, Antinus.

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway, NJ : IEEE, 2011. p. 4600-4604 (Proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Vol. 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - A subject-independent brain-computer interface based on smoothed, second-order baselining.

AU - Reuderink, B.

AU - Farquhar, Jason

AU - Poel, Mannes

AU - Nijholt, Antinus

N1 - Conference code: 33

PY - 2011/9/3

Y1 - 2011/9/3

N2 - A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Traditional approaches to BCIs require the user to train for weeks or even months to learn to control the BCI. In contrast, BCIs based on machine learning only require a calibration session of less than an hour before the system can be used, since the machine adapts to the user's existing brain signals. However, this calibration session has to be repeated before each use of the BCI due to inter-session variability, which makes using a BCI still a time-consuming and an error-prone enterprise. In this work, we present a second-order baselining procedure that reduces these variations, and enables the creation of a BCI that can be applied to new subjects without such a calibration session. The method was validated with a motor-imagery classification task performed by 109 subjects. Results showed that our subject-independent BCI without calibration performs as well as the popular common spatial patterns (CSP)-based BCI that does use a calibration session.

AB - A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Traditional approaches to BCIs require the user to train for weeks or even months to learn to control the BCI. In contrast, BCIs based on machine learning only require a calibration session of less than an hour before the system can be used, since the machine adapts to the user's existing brain signals. However, this calibration session has to be repeated before each use of the BCI due to inter-session variability, which makes using a BCI still a time-consuming and an error-prone enterprise. In this work, we present a second-order baselining procedure that reduces these variations, and enables the creation of a BCI that can be applied to new subjects without such a calibration session. The method was validated with a motor-imagery classification task performed by 109 subjects. Results showed that our subject-independent BCI without calibration performs as well as the popular common spatial patterns (CSP)-based BCI that does use a calibration session.

KW - Machine learning

KW - Brain-Computer Interface (BCI)

KW - Training

KW - HMI-CI: Computational Intelligence

KW - Calibration

UR - http://www.scopus.com/inward/record.url?scp=84863605471&partnerID=8YFLogxK

U2 - 10.1109/IEMBS.2011.6091139

DO - 10.1109/IEMBS.2011.6091139

M3 - Conference contribution

C2 - 22255362

SN - 978-1-4244-4121-1

T3 - Proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society

SP - 4600

EP - 4604

BT - 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

PB - IEEE

CY - Piscataway, NJ

T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011

Y2 - 30 August 2011 through 3 September 2011

ER -

Reuderink B, Farquhar J, Poel M , Nijholt A. A subject-independent brain-computer interface based on smoothed, second-order baselining. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway, NJ: IEEE. 2011. p. 4600-4604. (Proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society). https://doi.org/10.1109/IEMBS.2011.6091139

A subject-independent brain-computer interface based on smoothed, second-order baselining.

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this