Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.

[locked-in syndrome]

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 .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 .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 .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 .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 .