Effects of text generation on P300 brain-computer interface performance

• Published in: Brain computer interfaces (Abingdon, England) Vol. 3; no. 2; pp. 112 - 120
• Main Authors: Huggins, Jane E., Alcaide-Aguirre, Ramses E., Hill, Katya
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 01.01.2016
• Subjects: BCI; Brain-computer interface; communication; latency jitter; mental workload; novel text generation; P300; Brain-computer Interface; P300; BCI; Mental Workload; Novel text generation; Communication; Latency jitter
• ISSN: 2326-263X; 2326-2621; 2326-2621
• DOI: 10.1080/2326263X.2016.1203629

Brain-computer interfaces (BCIs) are intended to provide independent communication for those with the most severe physical impairments. However, development and testing of BCIs is typically conducted with copy-spelling of provided text, which models only a small portion of a functional communication task. This study was designed to determine how BCI performance is affected by novel text generation. We used a within-subject single-session study design in which subjects used a BCI to perform copy-spelling of provided text and to generate self-composed text to describe a picture. Additional off-line analysis was performed to identify changes in the event-related potentials that the BCI detects and to examine the effects of training the BCI classifier on task-specific data. Accuracy was reduced during the picture description task; (t(8) = 2.59 p = .0321). Creating the classifier using self-generated text data significantly improved accuracy on these data; (t(7) = −2.68, p = .0317), but did not bring performance up to the level achieved during copy-spelling. Thus, this study shows that the task for which the BCI is used makes a difference in BCI accuracy. Task-specific BCI classifiers are a first step to counteract this effect, but additional study is needed.