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Objective: Various augmentative and alternative communication (AAC) devices have been developed in order to aid communication for individuals with communication disorders. Recently, there has been interest in combining EEG data and eye-gaze data with the goal of developing a hybrid (or 'fused') BCI (hBCI) AAC system. This work explores the effectiveness of a speller that fuses data from an eye-tracker and the P300 speller in order to create a hybrid P300 speller., Approach: This hybrid speller collects both eye-tracking and EEG data in parallel, and the user spells characters on the screen in the same way that they would if they were only using the P300 speller. Online and offline experiments were performed. The online experiments measured the performance of the speller for sixteen non-disabled participants, while the offline simulations were used to assess the robustness of the hybrid system., Main Results: Online results showed that for fifteen non-disabled participants, using eye-gaze in a Bayesian framework with EEG data from the P300 speller improved accuracy ([Formula: see text], [Formula: see text], [Formula: see text] for estimated, medium and high variance configurations) and reduced the average number of flashes required to spell a character compared to the standard P300 speller that relies solely on EEG data ([Formula: see text], [Formula: see text], [Formula: see text] for estimated, medium and high variance configurations). Offline simulations indicate that the system provides more robust performance than a standalone eye gaze system., Significance: The results of this work on non-disabled participants shows the potential efficacy of hybrid P300 and eye-tracker speller. Further validation on the amyotrophic lateral sceloris population is needed to assess the benefit of this hybrid system.

Objective: Dry electrodes have an advantage over gel-based 'wet' electrodes by providing quicker set-up time for electroencephalography recording; however, the potentially poorer contact can result in noisier recordings. We examine the impact that this may have on brain-computer interface communication and potential approaches for mitigation., Approach: We present a performance comparison of wet and dry electrodes for use with the P300 speller system in both healthy participants and participants with communication disabilities (ALS and PLS), and investigate the potential for a data-driven dynamic data collection algorithm to compensate for the lower signal-to-noise ratio (SNR) in dry systems., Main Results: Performance results from sixteen healthy participants obtained in the standard static data collection environment demonstrate a substantial loss in accuracy with the dry system. Using a dynamic stopping algorithm, performance may have been improved by collecting more data in the dry system for ten healthy participants and eight participants with communication disabilities; however, the algorithm did not fully compensate for the lower SNR of the dry system. An analysis of the wet and dry system recordings revealed that delta and theta frequency band power (0.1-4 Hz and 4-8 Hz, respectively) are consistently higher in dry system recordings across participants, indicating that transient and drift artifacts may be an issue for dry systems., Significance: Using dry electrodes is desirable for reduced set-up time; however, this study demonstrates that online performance is significantly poorer than for wet electrodes for users with and without disabilities. We test a new application of dynamic stopping algorithms to compensate for poorer SNR. Dynamic stopping improved dry system performance; however, further signal processing efforts are likely necessary for full mitigation.

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 (EEG) 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 (SNR) 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 SNR of a user's EEG 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 (DS) (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 DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS 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.