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Using the detectability index to predict P300 speller performance.

Publication ,  Journal Article
Mainsah, BO; Collins, LM; Throckmorton, CS
Published in: Journal of neural engineering
December 2016

The P300 speller is a popular brain-computer interface (BCI) system that has been investigated as a potential communication alternative for individuals with severe neuromuscular limitations. To achieve acceptable accuracy levels for communication, the system requires repeated data measurements in a given signal condition to enhance the signal-to-noise ratio of elicited brain responses. These elicited brain responses, which are used as control signals, are embedded in noisy electroencephalography (EEG) data. The discriminability between target and non-target EEG responses defines a user's performance with the system. A previous P300 speller model has been proposed to estimate system accuracy given a certain amount of data collection. However, the approach was limited to a static stopping algorithm, i.e. averaging over a fixed number of measurements, and the row-column paradigm. A generalized method that is also applicable to dynamic stopping (DS) algorithms and other stimulus paradigms is desirable.We developed a new probabilistic model-based approach to predicting BCI performance, where performance functions can be derived analytically or via Monte Carlo methods. Within this framework, we introduce a new model for the P300 speller with the Bayesian DS algorithm, by simplifying a multi-hypothesis to a binary hypothesis problem using the likelihood ratio test. Under a normality assumption, the performance functions for the Bayesian algorithm can be parameterized with the detectability index, a measure which quantifies the discriminability between target and non-target EEG responses.Simulations with synthetic and empirical data provided initial verification of the proposed method of estimating performance with Bayesian DS using the detectability index. Analysis of results from previous online studies validated the proposed method.The proposed method could serve as a useful tool to initially assess BCI performance without extensive online testing, in order to estimate the amount of data required to achieve a desired accuracy level.

Duke Scholars

Published In

Journal of neural engineering

DOI

EISSN

1741-2552

ISSN

1741-2560

Publication Date

December 2016

Volume

13

Issue

6

Start / End Page

066007

Related Subject Headings

  • Reproducibility of Results
  • Monte Carlo Method
  • Models, Theoretical
  • Models, Statistical
  • Likelihood Functions
  • Humans
  • Event-Related Potentials, P300
  • Electroencephalography
  • Communication Aids for Disabled
  • Brain-Computer Interfaces
 

Citation

APA
Chicago
ICMJE
MLA
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Mainsah, B. O., Collins, L. M., & Throckmorton, C. S. (2016). Using the detectability index to predict P300 speller performance. Journal of Neural Engineering, 13(6), 066007. https://doi.org/10.1088/1741-2560/13/6/066007
Mainsah, B. O., L. M. Collins, and C. S. Throckmorton. “Using the detectability index to predict P300 speller performance.Journal of Neural Engineering 13, no. 6 (December 2016): 066007. https://doi.org/10.1088/1741-2560/13/6/066007.
Mainsah BO, Collins LM, Throckmorton CS. Using the detectability index to predict P300 speller performance. Journal of neural engineering. 2016 Dec;13(6):066007.
Mainsah, B. O., et al. “Using the detectability index to predict P300 speller performance.Journal of Neural Engineering, vol. 13, no. 6, Dec. 2016, p. 066007. Epmc, doi:10.1088/1741-2560/13/6/066007.
Mainsah BO, Collins LM, Throckmorton CS. Using the detectability index to predict P300 speller performance. Journal of neural engineering. 2016 Dec;13(6):066007.
Journal cover image

Published In

Journal of neural engineering

DOI

EISSN

1741-2552

ISSN

1741-2560

Publication Date

December 2016

Volume

13

Issue

6

Start / End Page

066007

Related Subject Headings

  • Reproducibility of Results
  • Monte Carlo Method
  • Models, Theoretical
  • Models, Statistical
  • Likelihood Functions
  • Humans
  • Event-Related Potentials, P300
  • Electroencephalography
  • Communication Aids for Disabled
  • Brain-Computer Interfaces