Article

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.

Langue : ANGLAIS