Abstract
| Original language | Undefined |
|---|---|
| Title of host publication | Proceedings 33rd Annual IEEE Conference on Engineering in Medicine and Biology (EMBC) |
| Place of Publication | USA |
| Publisher | IEEE Engineering in Medicine and Biology Society |
| Pages | 4600-4604 |
| Number of pages | 5 |
| ISBN (Print) | 978-1-4244-4121-1 |
| DOIs | |
| 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 | |
|---|---|
| Publisher | IEEE Engineering in Medicine & Biology Society |
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
- METIS-281610
- Machine Learning
- Brain-Computer Interface
- EWI-20919
- IR-78787
- Training
- HMI-CI: Computational Intelligence
- Calibration
Cite this
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A subject-independent brain-computer interface based on smoothed, second-order baselining. / Reuderink, B.; Farquhar, Jason; Poel, Mannes; Nijholt, Antinus.
Proceedings 33rd Annual IEEE Conference on Engineering in Medicine and Biology (EMBC). USA : IEEE Engineering in Medicine and Biology Society, 2011. p. 4600-4604.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 - eemcs-eprint-20919
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 subjectindependent 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 subjectindependent BCI without calibration performs as well as the popular common spatial patterns (CSP)-based BCI that does use a calibration session.
KW - METIS-281610
KW - Machine Learning
KW - Brain-Computer Interface
KW - EWI-20919
KW - IR-78787
KW - Training
KW - HMI-CI: Computational Intelligence
KW - Calibration
U2 - 10.1109/IEMBS.2011.6091139
DO - 10.1109/IEMBS.2011.6091139
M3 - Conference contribution
SN - 978-1-4244-4121-1
SP - 4600
EP - 4604
BT - Proceedings 33rd Annual IEEE Conference on Engineering in Medicine and Biology (EMBC)
PB - IEEE Engineering in Medicine and Biology Society
CY - USA
ER -