Enabling Fast Brain-Computer Interaction by Single-Trial Extraction of Visual Evoked Potentials

This paper investigates the challenging issue of enabling fast brain-computer interaction to construct a mental speller. Exploiting visual evoked potentials as communication carriers, an online paradigm called "imitating-human-natural-reading" is realized. In this online paradigm, single-trial estimation with the intrinsically real-time feature should be used instead of grand average that is traditionally used in the cognitive or clinical experiments. By the use of several montages of component features from four channels with parameter optimization, we explored the support vector machines-based single-trial estimation of evoked potentials. The results on a human-subject show the advantages of the inducing paradigm used in our mental speller with a high classification rate.