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Objective The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. Approach We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute signal-to-noise ratio of a user’s electroencephalography data. We further enhanced the algorithm by incorporating information about the user’s language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. Main Results Results from online testing of the dynamic stopping algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/sec (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the dynamic stopping algorithms. Significance We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.

Historically, access in augmentative and alternative communication (AAC) has been conceptualized as the physical operation of AAC technologies; more recently, research and development in the cognitive and social sciences has helped to broaden the concept to include a range of human factors involved in the successful use of AAC technologies in social interactions. The goal of this article is to expand the current understanding of communication access by providing a conceptual framework for examining AAC access, evaluating recent scientific and technical advances in the areas of AAC, and discussing the challenges to accessing AAC technologies for a range of communication activities.

Introduction The aim of this research was to evaluate the impact of a novel tele-rehabilitation system on self-reported functional outcomes compared to usual care during the first three months after stroke. Methods A parallel, two-arm, evaluator-blinded, randomised controlled trial was conducted. Adults aged ≥40 years who had suffered a stroke within four weeks of the start of the study were recruited from the general community. The intervention group received access to a novel tele-rehabilitation system and programme for three months. The primary outcome measures utilised were the frequency and limitation total scores of the Late-Life Function and Disability Instrument (LLFDI) at three months. Results A total of 124 individuals were recruited. The mean differences in the LLDFI frequency and limitation total scores at three months comparing the intervention and control groups were –3.30 (95% confidence interval (CI) –7.81 to 1.21) and –6.90 (95% CI –15.02 to 1.22), respectively. Adjusting for the respective baseline covariates and baseline Barthel Index also showed no significant difference between interventions in the LLFDI outcomes. Discussion The intervention and control groups self-reported similar improvements in functional outcomes. Tele-rehabilitation may be a viable option to provide post-stroke rehabilitation services in Singapore while reducing barriers to continue rehabilitation conventionally after discharge from hospital and encouraging more participation.

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