Companies like Meta, Apple, Microsoft, Neuralink, and emerging startups are rapidly developing physiological computing devices that read muscle, brain, and/or eye movements. This industry boom creates urgent demand for professionals trained in neural interfaces and physiological computing, yet most computing programs lack standardized curricula in this area. This BOF will discuss two critical questions: How can we develop a framework for standardized physiological computing courses across computing curricula? What training and resources do faculty need to confidently teach these interdisciplinary topics?

Brain-Computer Interface (BCI)-based authentication offers a promising alternative to traditional passwords, especially in collaborative environments where close proximity increases the risk of spoofing. Since BCI systems rely on brain signals that cannot be externally observed, they enhance security by eliminating the need for visible password inputs. We present a longitudinal study using EEG signals in a closed-loop, real-time P300-based BCI authentication system. Volunteers completed multiple login sessions over two weeks using a consistent visual stimulus pattern. We evaluate the system’s performance across three aspects: (1) how authentication scores vary over time for the same user, (2) how accurately the system verifies genuine login attempts, and (3) how well it rejects impostor attempts using the same pattern. Pearson correlation was most effective for matching the same user over time, while Chebyshev distance best distinguished between different users.

As the development of augmented reality (AR) and virtual reality (VR) applications is still limited to those with substantial amounts of technical knowledge, the prototyping and testing of user interface (UI) designs for AR and VR applications remotely proves difficult. Recent tools proposed for prototyping AR/VR applications focus on working toward increased fidelity of prototyping methods, but provide limited ability to easily collect objective quantitative data from user interactions with prototypes, especially in remote settings. In this paper, we present a remote usability study using Adobe XD rapid prototyping software integrated with Maze User Testing Software, that collect data for UI designs for an existing cross-platform software. The results were found by collecting task completion time, misclick rate, and user click-data using heatmaps. We discuss the results of our study, and show that objective quantitative data can be collected for AR/VR prototypes in remote testing settings to provide insightful usability feedback in early stages of interface design.

The rise of security concerns has spurred on-going research into Brain-Computer Interfaces (BCI) based authentication. These applications utilize electroencephalogram (EEG) signals, due to their properties that can enhance security systems. In previous studies, EEG data has been incorporated into various authentication systems to compare the performance of new and existing classification methods. However, using EEG data to compare the performance of distance metrics in a P300-based BCI authentication system has not been explored yet. In this study, EEG data is used to determine the most effective distance metric for authenticating users in a closed-loop system. To accomplish this task, we conducted a longitudinal study to evaluate three distance metrics (Cosine, Correlation and Chebyshev) while participants interacted with our BCI authentication system. Our results indicated that the Cosine similarity outperformed all other distance metrics for each user.

While researchers have found benefits in the use of Augmented Reality (AR) for training in maintenance tasks, many existing applications are limited to the use of hand-held controllers for interaction with the virtual environment. One novel alternative to traditional controls is the use of Brain-Machine Interface (BMI) systems and Motor Imagery (MI), using data from the user’s brain in the form of electroencephalography (EEG) waves. While some research has explored the use of BMI with virtual environments, the use of MI in an AR system has not been explored for use in training how to accomplish a procedural task. Therefore, this paper presents a BMI system combined with AR, to create a framework that allows for hands-free traversal of a predefined instruction-set for training using user brain activity. The created prototype allows for control of an AR environment using three modalities: brain activity, voice commands, and hand gestures. The created BMI-AR prototype allows for control of navigation through a predetermined instruction set using a user’s brain waves or voice commands, as well as allow users to see their passive EEG readings of affective mental state in real time. The initial prototype resulted in promising initial machine learning (ML) accuracy scores for motor imagery (MI) tasks.

This paper presents a virtual reality (VR) application that allows users to view a series of 360 degree videos, depicting bystander intervention scenarios, from a bystander perspective. Bystander intervention is a commonly used training on how to prevent and de-escalate potentially harmful or violent scenarios [5]. This application enables users to witness, from a first-hand perspective, a successful bystander intervention strategy being used by another person. This paper discusses motivations for creating such an application by giving an overview of the state of the art in bystander intervention training methods. It also discusses the application flow and design of the created system. Additionally, a preliminary user study was conducted to gain initial feedback and user perspectives on the system.

Software is a critical component of brain-computer interfaces (BCIs). While BCI hardware enables the retrieval of brain signals, BCI software is required to analyze these signals, produce output, and provide feedback. Users from multiple research areas have adopted BCI software platforms to investigate various concepts. Recently, interest in web-based BCI software has also emerged. The system design and control signal techniques of state-of-the-art BCI software platforms have been previously investigated. However, there is limited literature discussing user adoption of BCI software platforms. Additionally, there is a lack of work discussing the recent emergence of web tools relevant to BCI applications. This article aims to address these gaps by presenting a bibliometric review of the state-of-the-art BCI software. Furthermore, we discuss web-based BCIs and present tools that may be used to develop future web-based BCI applications.

A research study measuring perceptions of computing held by Hispanic high school students has been conducted using two visual programming interfaces. Undergraduates conducted regular weekend classes using both Alice and App Inventor lessons to provide computing instruction to the students enrolled in an enrichment program. The goal of this research was to identify and measure high school student perceptions of computing after being introduced computing using drag-drop programming interfaces. The results of this work demonstrates how student interest in computing increases once exposed to computing, but the interest increment is not enough for them to major in a computing area, highlighting the importance of ongoing engagement in computing throughout the high school years.