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Abstract
Spelling is an important application of brain-computer interfaces (BCIs). Previous BCI spellers were not suited for widespread use due to their low information transfer rate (ITR). In this study, we constructed a high-ITR BCI speller based on the steady-state visual evoked potential (SSVEP). A 45-target BCI speller was implemented with a frequency resolution of 0.2 Hz. A sampled sinusoidal stimulation method was used to present visual stimuli on a conventional LCD screen. The online results revealed that the proposed BCI speller had a good performance, reaching a high average accuracy (84.1% for 2 s stimulation time; 90.2% for 3 s stimulation time) and the corresponding high ITR (105 bits/min for 2 s stimulation time, 82 bits/min for 3 s stimulation time) during the low-frequency stimuli, while 88.7% and 61 bits/min were achieved for a 4 s time window during the high-frequency stimuli.
Acknowledgements
This work was supported by the National Basic Research Program (973) of China (No. 2011CB933204), National Natural Science Foundation of China under Grant 90820304, 91120007, Chinese 863 Project: 2012AA011601. We would like to thank Ian Daly of the University of Reading for his help in language editing.