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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Reuderink, B. Farquhar, J. Poel, M. Nijholt, A. |
| Copyright Year | 2011 |
| Description | Author affiliation: Human Media Interaction, Faculty of EEMCS, University of Twente, The Netherlands (Reuderink, B.; Poel, M.; Nijholt, A.) || Cognitive Artificial Intelligence, NICI, Radboud University Nijmegen, The Netherlands (Farquhar, J.) |
| 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. |
| Starting Page | 4600 |
| Ending Page | 4604 |
| File Size | 1174849 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424441211 |
| ISSN | 1557170X |
| e-ISBN | 9781457715891 |
| e-ISBN | 9781424441228 |
| DOI | 10.1109/IEMBS.2011.6091139 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-08-30 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Silicon Support vector machines Pipelines Calibration Training Transforms Electroencephalography |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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