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Abstract Objective.The RSVP Keyboard is a non-implantable, event-related potential-based brain-computer interface (BCI) system designed to support communication access for people with severe speech and physical impairments. Here we introduce inquiry preview (IP), a new RSVP Keyboard interface incorporating switch input for users with some voluntary motor function, and describe its effects on typing performance and other outcomes.Approach.Four individuals with disabilities participated in the collaborative design of possible switch input applications for the RSVP Keyboard, leading to the development of IP and a method of fusing switch input with language model and electroencephalography (EEG) evidence for typing. Twenty-four participants without disabilities and one potential end user with incomplete locked-in syndrome took part in two experiments investigating the effects of IP and two modes of switch input on typing accuracy and speed during a copy-spelling task.Main results.For participants without disabilities, IP and switch input tended to worsen typing performance compared to the standard RSVP Keyboard condition, with more consistent effects across participants for speed than for accuracy. However, there was considerable variability, with some participants demonstrating improved typing performance and better user experience (UX) with IP and switch input. Typing performance for the potential end user was comparable to that of participants without disabilities. He typed most quickly and accurately with IP and switch input and gave favorable UX ratings to those conditions, but preferred standard RSVP Keyboard.Significance.IP is a novel multimodal interface for the RSVP Keyboard BCI, incorporating switch input as an additional control signal. Typing performance and UX and preference varied widely across participants, reinforcing the need for flexible, customizable BCI systems that can adapt to individual users. ClinicalTrials.gov Identifier: NCT04468919. 

Autism is a neurodevelopmental disability that impacts one’s social communication and interaction. When left unsupported, this can increase the amount of loneliness felt by autistic people. Communication technology, such as AAC, can be helpful in supporting social communication, especially when co-designed with autistic people. We conducted a series of design workshops to co-design a new AAC system specifically supporting social communication. In this paper, we focus on the accessibility issues that were identified when running our workshops and provide recommendations on how to improve the process. We found that it is critical to build support for information processing time into the workshops, include a variety of AAC stakeholders, and create a shared vocabulary between the workshop participants to make design workshops more accessible to autistic adults. 

Augmentative and alternative communication (AAC) is a field of research and practice that works with people who have a communication disability. One form AAC can take is a high-tech tool, such as a software-based communication system. Like all user interfaces, these systems must be designed and it is critical to include AAC users in the design process for their systems. A participatory design approach can include AAC users in the design process, but modifications may be necessary to make these methods more accessible. We present a two-part design process we are investigating for improving the participatory design for high-tech AAC systems. We discuss our plans to refine the accessibility of this process based on participant feedback. 

Communication Brain-Computer Interfaces (cBCIs) represent a crucial technological advancement for individuals with severe motor disabilities as they offer a direct pathway to express their thoughts and needs without physical movement. These systems commonly leverage the P300 ERP, a distinct neural response approximately 300-500ms after a novel stimulus. Language modeling presents a promising approach to enhancing the performance and usability of cBCIs. However, integrating language models with cBCI systems presents unique challenges, including balancing model complexity with real-time processing requirements and optimizing system performance parameters. This study utilizes simulations of online cBCI data to investigate the impact of different language models on typing rate and accuracy. 

Text entry is a common and important part of many intelligent user interfaces. However, inferring a user’s intended text from their input can be challenging: motor actions can be imprecise, input sensors can be noisy, and situations or disabilities can hamper a user’s perception of interface feedback. Numerous prior studies have explored input on touchscreen phones, smartwatches, in midair, and on desktop keyboards. Based on these prior studies, we are releasing a large and diverse data set of noisy typing input consisting of thousands of sentences written by hundreds of users on QWERTY-layout keyboards. This paper describes the various subsets contained in this new research dataset as well as the data format. 

Some individuals with motor impairments communicate using a single switch - such as a button click, air puff, or blink. Row-column scanning provides a method for choosing items arranged in a grid using a single switch. An alternative, Nomon, allows potential selections to be arranged arbitrarily rather than requiring a grid (as desired for gaming, drawing, etc.) - and provides an alternative probabilistic selection method. While past results suggest that Nomon may be faster and easier to use than row-column scanning, no work has yet quantified performance of the two methods over longer time periods or in tasks beyond writing. In this paper, we also develop and validate a webcam-based switch that allows a user without a motor impairment to approximate the response times of a motor-impaired single switch user; although the approximation is not a replacement for testing with single-switch users, it allows us to better initialize, calibrate, and evaluate our method. Over 10 sessions with the webcam switch, we found users typed faster and more easily with Nomon than with row-column scanning. The benefits of Nomon were even more pronounced in a picture-selection task. Evaluation and feedback from a motor-impaired switch user further supports the promise of Nomon. 

Some individuals with motor impairments communicate using a single switch - such as a button click, air puff, or blink. Our software, Nomon, provides a method for single-switch users to select between items on a screen. Nomon’s flexibility stems from its probabilistic selection method, which allows potential options to be arranged arbitrarily rather than requiring they be arranged in a grid. As a result, Nomon can be used for a host of applications - including gaming, drawing, and web browsing. Focusing on accessibility, we updated the Nomon interface in collaboration with a switch user and with experts in Augmentative and Alternative Communication (AAC). We present our updated Nomon interface as an open-source web application. 

Word predictions in a text entry interface can help accelerate a user’s input. This may especially be true for users who have a slow input rate due to some form of motor-impairment. The choice of how many word predictions to offer in a text entry interface is an important design decision. In this work, we offered different number of word predictions in a keyboard where able-bodied users had to dwell on a key for one second to click it. We found participants’ text entry rate did not improve with increasing number of predictions.