title: Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.
abstract: 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.
  
  
  METHODS
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Duke University, Department of Electrical and Computer Engineering, USA.
authors.forename: B O
authors.initials: BO
authors.lastname: Mainsah
authors.forename: L M
authors.initials: LM
authors.lastname: Collins
authors.forename: K A
authors.initials: KA
authors.lastname: Colwell
authors.forename: E W
authors.initials: EW
authors.lastname: Sellers
authors.forename: D B
authors.initials: DB
authors.lastname: Ryan
authors.forename: K
authors.initials: K
authors.lastname: Caves
authors.forename: C S
authors.initials: CS
authors.lastname: Throckmorton
mesh_terms: D000328:Adult; D000465:Algorithms; D000690:Amyotrophic Lateral Sclerosis / Q000503:physiopathology* / Q000534:rehabilitation*; D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D003143:Communication Devices for People with Disabilities; D016287:Computer Peripherals; D004569:Electroencephalography / Q000379:methods*; D018913:Event-Related Potentials, P300; D005260:Female; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014793:Visual Cortex / Q000503:physiopathology; D014796:Visual Perception; D015443:Word Processing