Effects of text generation on P300 brain-computer interface performance

Abstract

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.

Keywords:

• Brain-computer interface
• communication
• BCI
• mental workload
• novel text generation
• P300
• latency jitter

Notes

1. ^{Electro-Cap International, Inc, Eaton, OH, USA}.

2. ^{Guger Technologies OEG, Graz, Austria}.

3. ^{BCI2000 v2.0 build 2104, www.bci2000.org, Albany, NY, USA}.

4. ^{Mayer-Johnson, Pittsburgh, PA, USA}.

5. ^{Microsoft, Redmond, WA, USA}.

6. AAC Institute, Pittsburgh, PA, USA.

7. IBM, Armonk, NY, USA.

8. SAS Institute Inc., Cary, NC, USA.