P300 Detection Based on EEG Shape Features

Abstract EN

We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system.

Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject’s P300 based on his/her own acquired signals.

Our experiments with 21 subjects showed that the SWLDA’s performance using our shape-feature vector was 93 % , that is, 10 % higher than the one obtained with BCI2000-feature’s vector.

The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity.

The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of 0.88 .

Also, most of the subjects needed less than 15 trials to have an AUROC superior to 0.8 .

Finally, we found that the electrode C4 also leads to better classification.