Foundation Models (FMs) have surged in popularity over the past five years, with applications spanning fields from computer vision to natural language processing. At the same time, Brain-Computer Interfaces (BCIs) have also gained momentum due to their potential to support individuals with complex disabilities. Among various BCI paradigms, code-modulated Visual Evoked Potentials (c-VEPs) remain relatively understudied, despite offering high information transfer rates and large selection target capacities. However, c-VEP systems require lengthy calibration sessions, significantly limiting their practicality, particularly outside of laboratory settings. In this study, we use a FM for the first time to eliminate the need for lengthy calibration in c-VEP BCI systems. We evaluated two approaches: (1) a truly calibration-free approach requiring no subject-specific data, and (2) a limited calibration approach, where we assessed the benefit of incorporating incremental amounts of calibration data. In both cases, a classification head is trained on data from other subjects. For a new subject, no calibration data is required in the calibration-free setup, making the c-VEP system effectively plug-and-play. The proposed method was tested on two c-VEP datasets. For the calibration-free approach, the average accuracy on the first dataset (n = 17) was 68.8% ± 17.6%, comparable to the full-calibration performance reported in the original study (66.2% ± 13.8%), which required approximately 11 minutes of calibration. On the second dataset (n = 12), the calibration-free accuracy was 71.8% ± 20.2%, versus 93.7% ± 5.5% from the original study, which required around 3.5 minutes. A limited-calibration approach using only 20% of the subject’s data (approximately 43 seconds) yielded 92%±5.2% accuracy. These results indicate that our FM–based approach can effectively eliminate or significantly reduce the need for lengthy calibration in c-VEP BCIs.

Children are an under-served population in the field of brain-computer interface (BCI) development. The high prevalence of lifelong disability coupled with the diversity and plasticity of children's brains make them ideal candidates for personalized BCI systems. Channel selection methods provide a tool for the in-session personalization of BCI systems. To evaluate the efficacy of channel selection for pediatric users, we tested four wrapper-based channel selection algorithms, sequential forward selection (SFS), sequential backward selection (SBS), sequential forward floating selection (SFFS), and sequential backward floating selection (SBFS) on offline motor imagery BCI data from three datasets involving typically developing children. The purpose was to assess the performance benefits and computational costs of each algorithm. All algorithms provided classification accuracy gains of 10–15 % with their optimal subsets. The time required to reach the optimal subsets varied between algorithms, but all took less than 80 s with mean completion times of 9.5 s and 35.8 s for the fastest (SFS) and slowest (SFFS), respectively. Adjusting the stopping criterion of the algorithm enables users to further reduce computation time with a disproportionately small effect on classification accuracy. All methods demonstrated an ability to prioritize expected physiological regions of interest and leave out channels detrimental to the classifier. Channel selection offers personalization of the BCI system for a specific user and a specific classifier. These findings emphasize the value of using personalized channel selection algorithms to improve motor imagery BCI systems for pediatric users.

There is a pressing need for alternative access technologies that enable children with severe physical disabilities, as current options often require some degree of controlled movement to be used efficiently. Brain-computer interfaces (BCIs) hold significant potential for improving the lives of children with severe physical disabilities, however research must prioritize user-centered approaches and real-world applications to maximize benefits. This paper examines the integration of home-based BCIs for children with quadriplegic cerebral palsy through user-centered design, focusing on the feasibility, usability, and impact on personal goals and activities of daily living. Seven children aged 6–15 years with quadriplegic cerebral palsy and their families participated in this pilot study, using personalized BCI packages and home-based virtual sessions to help them achieve individualized goals in self-care, productivity, and leisure. We utilized a collaborative goal-setting approach and assessed satisfaction and performance changes using the Canadian Occupational Performance Measure (COPM) and the BCI-adapted Quebec User Evaluation of Satisfaction with Assistive Technology (e-QUEST2.0). Significant improvements in performance and satisfaction were observed in the COPM scores, while parents were most satisfied with professional services and least satisfied with the adjustability of the BCI system, as per the eQuest2.0 questionnaire. Despite no significant improvement in BCI consistency across nine sessions, the intervention positively impacted participants' perceived performance and satisfaction in goal-oriented activities. Future research should focus on enhancing BCI design, comfort, and effectiveness while considering user priorities and feedback for personalized goal achievement.

We present a brain-computer interface (BCI) system designed to enable individuals with severe motor disabilities to play Boccia, a Paralympic sport. Boccia is a precision sport in which the objective is to get a ball as close as possible to a target. In its most adapted form, Boccia allows for the use of a ramp to assist the user. The proposed system consists of a BCI-enabled ramp that can be controlled by the user's brain signals using a visual control paradigm (i.e., P300, or SSVEP). We developed a software interface using custom tools in Unity and Python for the front-end and back-end, respectively. To validate the software, we tested the system with five subjects who performed six pipelines (three with P300 and three with SSVEP) to simulate real-world use. Each pipeline consisted of 10 guided selections in the software. The classifiers used Riemannian geometry and shrinkage linear discriminant analysis (sLDA) for P300 and canonical correlation analysis (CCA) for SSVEP. The results showed that the P300$(93\pm 3\ \%,\ \text{mean} \pm \text{SEM})$paradigm had higher classification accuracy than the SSVEP$(27\pm 0.02\%, \text{mean} \pm \text{SEM})$paradigm. Additionally, we designed and built a 3D CAD model and a hardware prototype of the ramp. The hardware prototype uses linear actuators to change the incline of the ramp and the height of the ball. Stepper motors allow for the rotation of the ramp and the release mechanism of the ball. Recommendations on improvements to the hardware and software components are made for future prototypes. The presented system opens new possibilities for sports applications that can improve the quality of life of people with severe motor disabilities.