Predictive Spelling With a P300-Based Brain–Computer Interface: Increasing the Rate of Communication

Abstract

This study compared a conventional P300 speller brain–computer interface (BCI) to one used in conjunction with a predictive spelling program. Performance differences in accuracy, bit rate, selections per minute, and output characters per minute (OCM) were examined. An 8 × 9 matrix of letters, numbers, and other keyboard commands was used. Participants (N = 24) were required to correctly complete the same 58 character sentence (i.e., correcting for errors) using the predictive speller (PS) and the nonpredictive speller (NS), counterbalanced. The PS produced significantly higher OCMs than the NS. Time to complete the task in the PS condition was 12 min 43 s as compared to 20 min 20 sec in the NS condition. Despite the marked improvement in overall output, accuracy was significantly higher in the NS paradigm. P300 amplitudes were significantly larger in the NS than in the PS paradigm, which is attributed to increased workload and task demands. These results demonstrate the potential efficacy of predictive spelling in the context of BCI.

Acknowledgments

This work has been supported by NIBIB & NINDS, NIH (EB00856), NIDCD, NIH (1 R21 DC010470-01), NIDCD, NIH (1 R15 DC011002-01). We thank Juliane Armstrong, James Bailey, Chris Hauser, Tiffany Lewis, and Kayla Winnen for data collection. We thank Peter Brunner, Geoff Bashore, and Steve Carmack for software that supports BCI2000.

Notes

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