Reducing Calibration Time for Brain-Computer Interfaces: A Clustering Approach
Abstract
Up to now even subjects that are experts in the use of machine learning based BCI systems still have to undergo a calibration session of about 20-30 min. From this data their (movement) intentions are so far infered. We now propose a new paradigm that allows to completely omit such calibration and instead transfer knowledge from prior sessions. To achieve this goal we first define normalized CSP features and distances in-between. Second, we derive prototypical features across sessions: (a) by clustering or (b) by feature concatenation methods. Finally, we construct a classifier based on these individualized prototypes and show that, indeed, classifiers can be successfully transferred to a new session for a number of subjects.
Cite
Text
Krauledat et al. "Reducing Calibration Time for Brain-Computer Interfaces: A Clustering Approach." Neural Information Processing Systems, 2006.Markdown
[Krauledat et al. "Reducing Calibration Time for Brain-Computer Interfaces: A Clustering Approach." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/krauledat2006neurips-reducing/)BibTeX
@inproceedings{krauledat2006neurips-reducing,
title = {{Reducing Calibration Time for Brain-Computer Interfaces: A Clustering Approach}},
author = {Krauledat, Matthias and Schröder, Michael and Blankertz, Benjamin and Müller, Klaus-Robert},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {753-760},
url = {https://mlanthology.org/neurips/2006/krauledat2006neurips-reducing/}
}