[Single-trial estimation of visual evoked potentials in single channel single-trial estimation].

We constructed a Brain-computer interface-based mental speller which realizes user-computer interaction. The feature signals of user's intention are embedded in spontaneous EEG background. Single-trial feature estimation should be used on this online occasion instead of the grand average usually used in cognitive or clinical experiments. To demonstrate this technique beyond laboratories, fewer EEG recording channels are preferred. A unique paradigm, which is called imitating-natural-reading, was exploited to induce visual evoked potentials. We explored the single-trial estimation of VEP recorded in single channel using support vector machine on three subjects, and obtained satisfactory data, the classification accuracy being 92.1%, 94.1% and 91.5%, respectively. These results put forward a significant step fowards the ultimate realization of our mental speller.