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Humans need communication. The desire to communicate remains one of the primary issues for people with locked-in syndrome (LIS). While many assistive and augmentative communication systems that use various physiological signals are available commercially, the need is not satisfactorily met. Brain interfaces, in particular, those that utilize event related potentials (ERP) in electroencephalography (EEG) to detect the intent of a person noninvasively, are emerging as a promising communication interface to meet this need where existing options are insufficient. Existing brain interfaces for typing use many repetitions of the visual stimuli in order to increase accuracy at the cost of speed. However, speed is also crucial and is an integral portion of peer-to-peer communication; a message that is not delivered timely often looses its importance. Consequently, we utilize rapid serial visual presentation (RSVP) in conjunction with language models in order to assist letter selection during the brain-typing process with the final goal of developing a system that achieves high accuracy and speed simultaneously. This paper presents initial results from the RSVP Keyboard system that is under development. These initial results on healthy and locked-in subjects show that single-trial or few-trial accurate letter selection may be possible with the RSVP Keyboard paradigm.

Brain-computer interfaces (BCIs) promise to provide a novel access channel for assistive technologies, including augmentative and alternative communication (AAC) systems, to people with severe speech and physical impairments (SSPI). Research on the subject has been accelerating significantly in the last decade and the research community took great strides toward making BCI-AAC a practical reality to individuals with SSPI. Nevertheless, the end goal has still not been reached and there is much work to be done to produce real-world-worthy systems that can be comfortably, conveniently, and reliably used by individuals with SSPI with help from their families and care givers who will need to maintain, setup, and debug the systems at home. This paper reviews reports in the BCI field that aim at AAC as the application domain with a consideration on both technical and clinical aspects.

Background Some non-invasive 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 non-invasive BCI that involves Rapid Serial Visual Presentation (RSVP) of symbols and a unique classifier with EEG and language model fusion. Methods The RSVP Keyboard™ was developed with several unique features. Individual letters are presented at 2.5 per sec. Computer classification of letters as targets or non-targets 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 1–4 times. Nine participants with LIS and nine 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 five incremental levels of difficulty, that increased by selecting phrases for which the utility of the language model decreased naturally. Results Six participants with LIS and nine controls completed the experiment. All LIS participants successfully mastered spelling at level one and one subject achieved level five. Six of nine control participants achieved level five. 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.

Objective To increase the symbol rate of the electroencephalography (EEG) based brain computer interface (BCI) typing systems by utilizing the context information. Approach Event related potentials (ERP) corresponding to a stimulus in EEG can be used to detect the intended target of a person for BCI. This paradigm is widely utilized to build letter-by-letter BCI typing systems. Nevertheless currently available BCI-typing systems still requires improvement due to low typing speeds. This is mainly due to the reliance on multiple repetitions before making a decision to achieve a higher typing accuracy. Another possible approach to increase the speed of typing while not significantly reducing the accuracy of typing is to use additional context information. In this paper, we study the effect of using a language model as additional evidence for intent detection. Bayesian fusion of an n-gram symbol model with the EEG features is proposed, and specifically regularized discriminant analysis ERP discriminant is used to obtain EEG-based features. The target detection accuracies are rigorously evaluated for varying language model orders, as well as the number of ERP-inducing repetitions. Main Results The results demonstrate that the language models contribute significantly to letter classification accuracy. For instance, we find that a single-trial ERP detection supported by a 4-gram language model may achieve the same performance as using 3-trial ERP classification for the non-initial letters of words. Significance Overall, fusion of evidence from EEG and language models yields a significant opportunity to increase the symbol rate of a BCI typing system.