A Brain Computer Interface methodology based on a visual P300 paradigm

Abstract-Brain Computer Interface (BCI) systems based on electroencephalography (EEG) open a new communication channel for people with severe motor disabilities, without recurring to the conventional motor output pathways. The very low signal-to-noise ratio and low spatial resolution still limits severely BCIs communication bandwidth. This paper presents the ongoing work toward the development of a BCI system for wheelchair steering. A full system based on a visual P300 oddball paradigm is proposed. The signal processing algorithms are computationally efficient and require a short phase training. Temporal features and EEG channels are selected through a Fisher criteria. For enhancement of signal-to-noise ratio and data dimensionality reduction, a spatial filter named Common Spatial Patterns is applied. This method is widely used for classification of motor imagery events, however it is not very often used for classification of event related potentials such as P300. In this paper we show that Common Spatial Patterns is an effective approach to improve P300 classification rates. In our approach, the input features for classification are the projections of the filtered data instead of the variance of the projections as typically used in motor imagery. Offline classification results, obtained with a Bayesian classifier, are presented showing the effectiveness of the overall methodology.