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The ability to communicate using language is a fundamental human function. When this ability is compromised, as it can be in neuromuscular diseases such as amyotrophic lateral sclerosis (ALS) and brainstem strokes, patients stand to lose a significant source of functional independence. Brain-computer interface (BCI) systems help restore communication to these "locked-in" patients, usually relying on P300 evoked response potentials (ERPs) to identify a target character among repetitive serial presentation of possible characters. While the so-called "P300 speller" was first described over 25 years ago, little overall progress has been made with respect to clinical implementation, with major system limitations related to practicality, speed, and accuracy. This work addresses these concerns by using machine learning techniques to optimize the system design, accelerate the character selection process, and integrate natural language domain knowledge into the classifier. This effort has involved several different projects, including selecting the optimal electrode positions using Gibbs sampling, performing unsupervised training with the Baum-Welch algorithm, and incorporating prior language knowledge using particle filtering. The result is an online system requiring only four electrodes that allows users to communicate at an average bit rate that is 75% higher than when using standard methods. These improvements can help to make the P300 speller system a more viable solution for "locked-in" patients, leading to increased functional independence and improved quality of life.
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