Brain Computer Interfaces (BCI) are systems enabling hu- mans to communicate with machines through signals generated by the brain. Several kinds of signals can be envisioned as well as means to mea- sure them. In this paper we are particularly interested in even-related brain potentials (ERP) and especially visually-evoked potential signals (P300) measured with surface electroencephalograms (EEG). When the human is stimulated with visual inputs, the P300 signals arise about 300 ms after the visual 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 ltering and SVMs. Experiments show that this method is a promising step toward single-trial detection of P300.
['Lucie Daubigney', 'Olivier Pietquin']
Single-trial P300 detection with Kalman filtering and SVMs.