Enhancing evoked responses for BCI through advanced ICA techniques

The P300 word speller is one of the important BCI applications which detects real-time P300 waveforms and translates them into letters (and then words) within a particular BCI paradigm. However due to the poor SNR of EEG, as well as the presence of other artifacts, the identification accuracy is still not high enough for real-world application. Here we present three slightly different approaches to improving performance based on ICA. When compared with the classification results obtained from the raw unprocessed data, the results using these approaches show distinct improvement. Furthermore, the results indicate that it is possible to reduce the number of epochs required to perform stimulus locked averages, whilst still maintaining good performance measures. This has the potential of speeding up the word speller and has further implications for use on similar ERP based systems