title: Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.
abstract: BACKGROUND
  Some noninvasive brain-computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation.
  
  
  OBJECTIVE
  To begin to address the communication needs of individuals with LIS using a noninvasive BCI that involves rapid serial visual presentation (RSVP) of symbols and a unique classifier with electroencephalography (EEG) and language model fusion.
  
  
  METHODS
  The RSVP Keyboard was developed with several unique features. Individual letters are presented at 2.5 per second. Computer classification of letters as targets or nontargets based on EEG is performed using machine learning that incorporates a language model for letter prediction via Bayesian fusion enabling targets to be presented only 1 to 4 times. Nine participants with LIS and 9 healthy controls were enrolled. After screening, subjects first calibrated the system, and then completed a series of balanced word generation mastery tasks that were designed with 5 incremental levels of difficulty, which increased by selecting phrases for which the utility of the language model decreased naturally.
  
  
  RESULTS
  Six participants with LIS and 9 controls completed the experiment. All LIS participants successfully mastered spelling at level 1 and one subject achieved level 5. Six of 9 control participants achieved level 5.
  
  
  CONCLUSIONS
  Individuals who have incomplete LIS may benefit from an EEG-based BCI system, which relies on EEG classification and a statistical language model. Steps to further improve the system are discussed.
authors.affiliation: 1Oregon Health & Science University, Portland, OR, USA.
authors.forename: Barry S
authors.initials: BS
authors.lastname: Oken
authors.forename: Umut
authors.initials: U
authors.lastname: Orhan
authors.forename: Brian
authors.initials: B
authors.lastname: Roark
authors.forename: Deniz
authors.initials: D
authors.lastname: Erdogmus
authors.forename: Andrew
authors.initials: A
authors.lastname: Fowler
authors.forename: Aimee
authors.initials: A
authors.lastname: Mooney
authors.forename: Betts
authors.initials: B
authors.lastname: Peters
authors.forename: Meghan
authors.initials: M
authors.lastname: Miller
authors.forename: Melanie B
authors.initials: MB
authors.lastname: Fried-Oken
mesh_terms: D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001921:Brain / Q000503:physiopathology*; D062207:Brain-Computer Interfaces; D003143:Communication Devices for People with Disabilities; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007802:Language; D008297:Male; D008875:Middle Aged; D011214:Practice, Psychological; D011782:Quadriplegia / Q000503:physiopathology / Q000534:rehabilitation*; D012815:Signal Processing, Computer-Assisted
keywords: brain–computer interface; electroencephalography (EEG); language model; locked-in syndrome; neuroengineering