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Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.

Publication ,  Journal Article
Mainsah, BO; Collins, LM; Colwell, KA; Sellers, EW; Ryan, DB; Caves, K; Throckmorton, CS
Published in: J Neural Eng
February 2015

OBJECTIVE: The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography (EEG) data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio (SNR) of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. APPROACH: We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute SNR of a user's EEG data. We further enhanced the algorithm by incorporating information about the user's language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (DS) (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. MAIN RESULTS: Results from online testing of the DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS algorithms. SIGNIFICANCE: We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.

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Published In

J Neural Eng

DOI

EISSN

1741-2552

Publication Date

February 2015

Volume

12

Issue

1

Start / End Page

016013

Location

England

Related Subject Headings

  • Word Processing
  • Visual Perception
  • Visual Cortex
  • Signal Processing, Computer-Assisted
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Middle Aged
  • Male
  • Information Storage and Retrieval
 

Citation

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Mainsah, B. O., Collins, L. M., Colwell, K. A., Sellers, E. W., Ryan, D. B., Caves, K., & Throckmorton, C. S. (2015). Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study. J Neural Eng, 12(1), 016013. https://doi.org/10.1088/1741-2560/12/1/016013
Mainsah, B. O., L. M. Collins, K. A. Colwell, E. W. Sellers, D. B. Ryan, K. Caves, and C. S. Throckmorton. “Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.J Neural Eng 12, no. 1 (February 2015): 016013. https://doi.org/10.1088/1741-2560/12/1/016013.
Mainsah BO, Collins LM, Colwell KA, Sellers EW, Ryan DB, Caves K, et al. Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study. J Neural Eng. 2015 Feb;12(1):016013.
Mainsah, B. O., et al. “Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.J Neural Eng, vol. 12, no. 1, Feb. 2015, p. 016013. Pubmed, doi:10.1088/1741-2560/12/1/016013.
Mainsah BO, Collins LM, Colwell KA, Sellers EW, Ryan DB, Caves K, Throckmorton CS. Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study. J Neural Eng. 2015 Feb;12(1):016013.
Journal cover image

Published In

J Neural Eng

DOI

EISSN

1741-2552

Publication Date

February 2015

Volume

12

Issue

1

Start / End Page

016013

Location

England

Related Subject Headings

  • Word Processing
  • Visual Perception
  • Visual Cortex
  • Signal Processing, Computer-Assisted
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Middle Aged
  • Male
  • Information Storage and Retrieval