Abstract
All brain-computer interface (BCI) groups that have published results of studies involving a large number of users performing BCI control based on the voluntary modulation of sensorimotor rhythms (SMR) report that BCI control could not be achieved by a non-negligible number of subjects (estimated 20% to 25%). This failure of the BCI system to read the intention of the user is one of the greatest problems and challenges in BCI research. There are two main causes for this problem in SMR-based BCI systems: either no idle SMR is observed over motor areas of the user, or this idle rhythm is not modulated during motor imagery, resulting in a classification performance lower than 70% (criterion level) that renders the control of a BCI application (like a speller) difficult or impossible. Previously, we introduced the concept of machine learning based co-adaptive calibration, which provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adaptive learning enables significant BCI control for completely novice users, as well as for those who could not achieve control with a conventional SMR-based BCI.
| Original language | English |
|---|---|
| Article number | 025009 |
| Journal | Journal of Neural Engineering |
| Volume | 8 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2011 Apr 1 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Biomedical Engineering
- Cellular and Molecular Neuroscience
Cite this
Co-adaptive calibration to improve BCI efficiency. / Vidaurre, Carmen; Sannelli, Claudia; Muller, Klaus; Blankertz, Benjamin.
In: Journal of Neural Engineering, Vol. 8, No. 2, 025009, 01.04.2011.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Co-adaptive calibration to improve BCI efficiency
AU - Vidaurre, Carmen
AU - Sannelli, Claudia
AU - Muller, Klaus
AU - Blankertz, Benjamin
PY - 2011/4/1
Y1 - 2011/4/1
N2 - All brain-computer interface (BCI) groups that have published results of studies involving a large number of users performing BCI control based on the voluntary modulation of sensorimotor rhythms (SMR) report that BCI control could not be achieved by a non-negligible number of subjects (estimated 20% to 25%). This failure of the BCI system to read the intention of the user is one of the greatest problems and challenges in BCI research. There are two main causes for this problem in SMR-based BCI systems: either no idle SMR is observed over motor areas of the user, or this idle rhythm is not modulated during motor imagery, resulting in a classification performance lower than 70% (criterion level) that renders the control of a BCI application (like a speller) difficult or impossible. Previously, we introduced the concept of machine learning based co-adaptive calibration, which provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adaptive learning enables significant BCI control for completely novice users, as well as for those who could not achieve control with a conventional SMR-based BCI.
AB - All brain-computer interface (BCI) groups that have published results of studies involving a large number of users performing BCI control based on the voluntary modulation of sensorimotor rhythms (SMR) report that BCI control could not be achieved by a non-negligible number of subjects (estimated 20% to 25%). This failure of the BCI system to read the intention of the user is one of the greatest problems and challenges in BCI research. There are two main causes for this problem in SMR-based BCI systems: either no idle SMR is observed over motor areas of the user, or this idle rhythm is not modulated during motor imagery, resulting in a classification performance lower than 70% (criterion level) that renders the control of a BCI application (like a speller) difficult or impossible. Previously, we introduced the concept of machine learning based co-adaptive calibration, which provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adaptive learning enables significant BCI control for completely novice users, as well as for those who could not achieve control with a conventional SMR-based BCI.
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U2 - 10.1088/1741-2560/8/2/025009
DO - 10.1088/1741-2560/8/2/025009
M3 - Article
C2 - 21436515
AN - SCOPUS:79954500834
VL - 8
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
SN - 1741-2560
IS - 2
M1 - 025009
ER -