Averaging techniques for single-trial analysis of oddball event-related potentials

More and more effort is done in BCI research to improve its usability for patients, with respect to its communication speed and transmission accuracy. In this contribution, we experiment with BCI speller based on P300 evoked potential. More precisely, the typical form of event-related potential (ERP) inspires us to devise classification methods based on the similarity/dissimilarity in the time domain between single trials and one or several estimated ERP templates derived from subject recordings. The reliable estimation of template is difficult in a single trial due to the low signal-to-noise ratio (SNR) of electroencephalographic (EEG) signals. We first explicitly estimate the template using several averaging techniques: pointto-point averaging, cross-correlation alignment and dynamic time warping. Then we inexplicitly estimate several ERP templates using learning vector quantization algorithm combined with an extreme learning machine. Finally classification is realized based on the similarity/dissimilarity between the single trials and the template. Simulation is carried out using a BCI competition III data set acquired with the P300 speller paradigm. The experiments show that template-based classifiers can also obtain high accuracy.