@InProceedings{Supelec679,
author = {Lucie Daubigney and Olivier Pietquin},
title = {{Single-pass P300 detection with Kalman filtering and SVMs}},
year = {2011},
booktitle = {{Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2011)}},
pages = {399-404},
month = {April},
address = {Bruges (Belgium)},
url = {http://www.metz.supelec.fr//metz/personnel/pietquin/pdf/ESANN_2011_LDOP.pdf},
abstract = {Brain Computer Interfaces (BCI) are systems enabling humans
to communicate with machines through signals generated by the
brain. Several kinds of signals can be envisioned as well as
means to measure them. In this paper we are particularly
interested in visually evoked potential signals (P300) measured
with surface electroencephalograms (EEG). These signals arise
when the human is stimulated with visual inputs 300 ms after
the stimulus has been received. Yet, the EEG signal is often
very noisy which makes the P300 detection hard. It is customary
to use an average of several trials to enhance the P300 signal
and reduce the random noise but this results in a lower bit
rate of the interface. In this contribution, we propose a novel
approach to P300 detection using Kalman filtering and SVMs.
Experiments show that this method allows single-pass detection
pass detections of P300.}
}