The performance of 9-11-year-old children using an SSVEP-based BCI for target selection

James J.S. Norton, Jessica Mullins, Birgit E. Alitz, Timothy Bretl

Research output: Contribution to journalArticlepeer-review


Objective. In this paper, we report the performance of 9-11-year-old children using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) and provide control data collected from adults for comparison. Children in our study achieved a much higher performance (79% accuracy; average age 9.64 years old) than the only previous investigation of children using an SSVEP-based BCI (∼50% accuracy; average age 9.86 years old). Approach. Experiments were conducted in two phases, a short calibration phase and a longer experimental phase. An offline analysis of the data collected during the calibration phase was used to set two parameters for a classifier and to screen participants who did not achieve a minimum accuracy of 85%. Main results. Eleven of the 14 children and all 11 of the adults who completed the calibration phase met the minimum accuracy requirement. During the experimental phase, children selected targets with a similar accuracy (79% for children versus 78% for adults), latency (2.1 s for children versus 1.9 s for adults), and bitrate (0.50 bits s-1 for children and 0.56 bits s-1 for adults) as adults. Significance. This study shows that children can use an SSVEP-based BCI with higher performance than previously believed and is the first to report the performance of children using an SSVEP-based BCI in terms of latency and bitrate. The results of this study imply that children with severe motor disabilities (such as locked-in syndrome) may use an SSVEP-based BCI to restore/replace the ability to communicate.

Original languageEnglish (US)
Article number056012
JournalJournal of Neural Engineering
Issue number5
StatePublished - Jul 27 2018


  • BCI
  • brain-computer interface
  • children
  • steady-state visual evoked potential

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience


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