A Brain Computer Interface with Online Feedback based on Magnetoencephalography
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The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signalto- noise ratio, is likely to succeed. We apply recursive channel elimination and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a trained classifier together with a decision tree interface to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best of our knowledge this paper is the first working online BCI based on MEG recordings and is therefore a proof of concept.
| Author(s): | Lal, TN. and Schröder, M. and Hill, J. and Preissl, H. and Hinterberger, T. and Mellinger, J. and Bogdan, M. and Rosenstiel, W. and Hofmann, T. and Birbaumer, N. and Schölkopf, B. |
| Links: | |
| Book Title: | Proceedings of the 22nd International Conference on Machine Learning |
| Pages: | 465-472 |
| Year: | 2005 |
| Day: | 0 |
| Editors: | L De Raedt and S Wrobel |
| Publisher: | ACM |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | New York, NY, USA |
| Event Name: | ICML 2005 |
| Event Place: | Bonn, Germany |
| Electronic Archiving: | grant_archive |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{3482,
title = {A Brain Computer Interface with Online Feedback based on Magnetoencephalography},
booktitle = {Proceedings of the 22nd International Conference on Machine Learning},
abstract = {The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signalto-
noise ratio, is likely to succeed. We apply recursive channel elimination and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a
trained classifier together with a decision tree interface to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best
of our knowledge this paper is the first working online BCI based on MEG recordings and is therefore a proof of concept.},
pages = {465-472},
editors = {L De Raedt and S Wrobel},
publisher = {ACM},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {New York, NY, USA},
year = {2005},
author = {Lal, TN. and Schr{\"o}der, M. and Hill, J. and Preissl, H. and Hinterberger, T. and Mellinger, J. and Bogdan, M. and Rosenstiel, W. and Hofmann, T. and Birbaumer, N. and Sch{\"o}lkopf, B.}
}