@article{klee2026measures,

title = {Measures of fatigue and performance are related to user interface and task in a communication BCI},

author = {Daniel Klee and Tab Memmott and Barry Oken},

doi = {10.1088/1741-2552/ae60d2},

year  = {2026},

date = {2026-04-16},

urldate = {2026-01-01},

journal = {Journal of Neural Engineering},

abstract = {Objective. This exploratory study compared two non-implantable Communication Brain-Computer Interfaces (cBCIs) to determine whether physiologic and self-report measures of mental fatigue, effort, and boredom were greater during calibration than during copy-spelling and whether there were differences between two common cBCI interfaces, Rapid Serial Visual Presentation (RSVP) and Single-Character Presentation Matrix (SCP-Matrix). Approach. Twenty-three healthy adults successfully utilized both RSVP and SCP-Matrix speller cBCIs in a single experimental session. Participants completed a calibration task and three online (closed-loop) copy-spelling tasks for each interface and provided self-report data on state mental fatigue, effort, and boredom. Physiological measures included EEG recordings alongside autonomic markers, including blood pressure, heart rate, respiration rate, and pulse rate variability (PRV). Main Results. Participants reported significant increases in perceived mental fatigue, effort, boredom, and sleepiness during the session, with significant increases during calibration compared to copy-spelling. On average, users typed 1.5 more correct characters per copy-spelling phase using the SCP-Matrix interface than when using RSVP. Results for autonomic and self-report metrics were consistent with fatigue being increased during calibration tasks relative to copy-spelling. EEG measures showed increased absolute and relative alpha activity and decreased relative theta activity during calibrations compared to copy-spelling, and increased absolute and relative alpha activity and decreased relative theta activity during RSVP, compared to Matrix. P300 amplitude on average was greater during copy spelling tasks than during calibrations. Significance. Participants demonstrated increased fatigue while using non-implantable cBCIs. Evidence suggested that calibration tasks for both interfaces were more fatiguing, required more mental effort, and were less engaging than copy-spelling tasks. Increased user fatigue and perceived mental effort remain significant barriers to sustained use of non-implantable cBCI systems. Though limited, the current study enhances our understanding of user experience with cBCIs and emphasizes the need to design more engaging and concise calibration procedures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{marquez2026improvement,

title = {Improvement of a BCI-enabled Boccia ramp through a patient engagement strategy},

author = {Daniel Comaduran Marquez and Daniella Bourque and Dejana Nikitovic and Alicia Hilderley and Eli Kinney-Lang and Iulian Ciobanu and Alison Levine and Adam Kirton},

doi = {10.70401/ec.2026.0020},

year  = {2026},

date = {2026-03-27},

urldate = {2026-01-01},

journal = {Empathic Computing},

volume = {2},

number = {2},

pages = {202531–202531},

publisher = {Science Exploration Press},

abstract = {Aims: The right to play is a basic human right. However, sport participation is often limited for children with complex motor disabilities. We developed a brain-computer interface (BCI)-enabled Boccia system that allows children with severe motor disabilities and communication difficulties to play independently. The purpose of this study was to partner with persons with lived experience (PWLE) to improve the BCI-Boccia system.



Methods: Following the Strategy for Patient-Oriented Research framework, we engaged seven PWLE. In the first session, we gathered comments from the PWLE, which were translated into a list of required features. The software was developed using an Agile approach. The second session involved a demonstration to collect additional feedback. In the third session, two PWLE tested the system in person. Engagement was evaluated using the Public and Patient Engagement Evaluation Tool (PPEET).



Results: Comments from the PWLE focused on improving the software controller and the mechanical stability of the ramp. New software controllers for coarse and fine movements were designed, and a new base was developed to enhance stability while allowing faster assembly and disassembly. The PPEET confirmed that PWLE felt their suggestions were considered and that sufficient resources were provided to support their participation.



Conclusion: We demonstrate that a patient engagement strategy can inform and facilitate improvements to a BCI-enabled Boccia system. Involving diverse PWLE throughout the design cycle may improve accessibility and user adoption in disability sports. Inclusive participation likely helps ensure that improvement efforts directly address the needs of end users},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{wang2026uav,

title = {UAV Target Detection and Tracking Integrating a Dynamic Brain–Computer Interface},

author = {Jun Wang and Zanyang Li and Lirong Yan and Muhammad Imtiaz and Hang Li and Muhammad Usman Shoukat and Jianatihan Jinsihan and Benjun Feng and Yi Yang and Fuwu Yan and others},

doi = {https://doi.org/10.3390/drones10030222},

year  = {2026},

date = {2026-03-20},

urldate = {2026-01-01},

journal = {Drones},

volume = {10},

number = {3},

pages = {222},

publisher = {MDPI},

abstract = {To address the inherent limitations in the robustness of fully autonomous unmanned aerial vehicle (UAV) visual perception and the high cognitive workload associated with manual control, this paper proposes a human-in-the-loop brain–computer interface (BCI) control framework. The system integrates steady-state visual evoked potential (SSVEP) with deep learning techniques to create a spatio-temporally dynamic interaction paradigm, enabling real-time alignment between visual targets and frequency stimuli. At the perception level, an enhanced YOLOv11 network incorporating partial convolution (PConv) and shape intersection over union (Shape-IoU) loss is developed and coupled with the DeepSort multi-object tracking algorithm. This configuration ensures high-speed execution on edge computing platforms while maintaining stable stimulus coverage over dynamic targets, thus providing a robust visual induction environment for EEG decoding. At the neural decoding level, an enhanced task-discriminant component analysis (TDCA-V) algorithm is introduced to improve signal detection stability within non-stationary flight conditions. Experimental results demonstrate that within the predefined fixation task window, the system achieves 100% success in maintaining target identity (ID). The BCI system achieved an average command recognition accuracy of 91.48% within a 1.0 s time window, with the TDCA-V algorithm significantly outperforming traditional spatial filtering methods in dynamic scenarios. These findings demonstrate the system’s effectiveness in decoupling human cognitive intent from machine execution, providing a robust solution for human–machine collaborative control.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gupta2025methodological,

title = {Methodological optimization for eliciting robust median nerve somatosensory evoked potentials for realtime single trial applications},

author = {Disha Gupta and Jodi Ann Brangaccio and NJ Hill},

doi = {10.1088/1741-2552/ae30ac},

year  = {2025},

date = {2025-12-23},

urldate = {2025-01-01},

journal = {Journal of Neural Engineering},

abstract = {Objective: Single-trial measurement of median nerve Somatosensory Evoked Potentials (SEPs) with noninvasive electroencephalography (EEG) is challenging due to low signal-to-noise ratio (SNR), limiting its use in real-time neurorehabilitation applications. We describe and evaluate methodological optimizations for eliciting reliable median nerve SEPs measurable in real time, with reduced reliance on post-processing.



Methods: In twelve healthy participants, two sessions each, SEPs were assessed at three pulse widths (0.1, 0.5, 1 msec), at a low-frequency stimulation (0.5 Hz ± 10%), and at an intensity sufficient to evoke consistent and robust sensory nerve action potentials (SNAPs) and compound muscle action potentials (CMAPs). The Evoked Potential Operant Conditioning System platform was used to monitor responses in real time. Feasibility was also evaluated in a participant with incomplete spinal cord injury (iSCI).



Results: SEP P50 and N70 were reliably elicited in healthy participants, and in individual with iSCI, across all tested pulse widths with minimal discomfort. N70 amplitude increased significantly with pulse width (χ2= 17.64, p= 0.0001, w= 0.80), while P50 amplitude remained unchanged. SNR showed a significant pulse width-dependent increase (χ2= 7.82, p= 0.02, w= 0.35) with improvements of 40% and 52% at 0.5 and 1 msec, respectively. N70 single-trial separability significantly improved at 1 msec (AUC of 0.83, χ2= 8.17, p= 0.017), including the iSCI participant (0.84-less impaired hand, 0.79-more impaired hand). Test-retest reliability (ICC= 0.70-0.84, p< 0.05) was highest at 0.5 msec, indicating more consistent N70 and P50 measurements across sessions at a longer pulse width.



Significance: Robust median nerve SEPs can be measured at single trials with methodological optimizations such as a longer pulse width (0.5-1ms), low frequency (0.5 Hz), a consistent afferent excitation guided by nerve and muscle responses, and a robust EEG acquisition system. This setup can be useful for real time SEP-based brain computer interface applications for rehabilitation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gupta2025extracting,

title = {Extracting Robust Single-Trial Somatosensory Evoked Potentials for Non-Invasive Brain Computer Interfaces},

author = {Disha Gupta and Jodi Ann Brangaccio and Helia Mojtabavi and Jonathan R Wolpaw and NJ Hill},

doi = {10.1088/1741-2552/adfd8a},

year  = {2025},

date = {2025-09-03},

urldate = {2025-01-01},

journal = {Journal of Neural Engineering},

abstract = {Objective. Reliable extraction of single-trial somatosensory evoked potentials (SEPs) is essential for developing brain-computer interface (BCI) applications to support rehabilitation after brain injury. For real-time feedback, these responses must be extracted prospectively on every trial, with minimal post-processing and artifact correction. However, noninvasive SEPs elicited by electrical stimulation at recommended parameter settings (0.1–0.2 msec pulse width, stimulation at or below motor threshold, 2–5 Hz frequency) are typically small and variable, often requiring averaging across multiple trials or extensive processing. Here, we describe and evaluate ways to optimize the stimulation setup to enhance the signal-to-noise ratio (SNR) of noninvasive single-trial SEPs, enabling more reliable extraction. Approach. SEPs were recorded with scalp electroencephalography in tibial nerve stimulation in thirteen healthy people, and two people with CNS injuries. Three stimulation frequencies (lower than recommended: 0.2 Hz, 1 Hz, 2 Hz) with a pulse width longer than recommended (1 msec), at a stimulation intensity based on H-reflex and M-wave at Soleus muscle were evaluated. Detectability of single-trial SEPs relative to background noise was tested offline and in a pseudo-online analysis, followed by a real-time demonstration. Main results. SEP N70 was observed predominantly at the central scalp regions. Online decoding performance was significantly higher with Laplacian filter. Generalization performance showed an expected degradation, at all frequencies, with an average decrease of 5.9% (multivariate) and 6.5% (univariate), with an AUC score ranging from 0.78–0.90. The difference across stimulation frequencies was not significant. In individuals with injuries, AUC of 0.86 (incomplete spinal cord injury) and 0.81 (stroke) was feasible. Real-time demonstration showed SEP detection with AUC of 0.89. Significance. This study describes and evaluates a system for extracting single-trial SEPs in real-time, suitable for a BCI-based operant conditioning. It enhances SNR of individual SEPs by alternate electrical stimulation parameters, dry headset, and optimized signal processing.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{țenea2025co,

title = {The co-created city: neuroadaptive design for healthy environments},

author = {Sabin-Andrei Țenea and Alexandru Berceanu and Sergiu Nisioi and Andreea Robu-Movilă and Constantin Pistol and Grigore Burloiu},

doi = {https://doi.org/10.1080/17508975.2025.2542804},

year  = {2025},

date = {2025-08-21},

urldate = {2025-01-01},

journal = {Intelligent Buildings International},

pages = {1–17},

publisher = {Taylor & Francis},

abstract = {Urban environments profoundly influence human well-being and behavior, underscoring the need for design paradigms that seamlessly integrate ecological principles with stakeholder requirements. This study investigates the convergence of affective computing and generative design to develop adaptive, health-promoting urban environments. Initially, a genetic-generative algorithm was created to generate an extensive database of high-rise tower designs optimized for solar exposure and surface-to-volume ratio. Subsequently, an EEG-based Brain–Computer Interface (BCI) system was implemented to capture architects’ subconscious emotional responses to selected designs using Event-Related Potentials (ERPs) and Self-Assessment Manikin (SAM) scales. EEG data from 24 participants were analyzed to extract ERP markers of valence-based preference, revealing significant neural responses in early (250–350 ms) and late (600–800 ms) time windows. A random forest model, complemented by SHAP analysis, demonstrated nonlinear influences of critical design parameters on subjective preference. EEG-derived preference scores were then integrated into a multi-objective optimization workflow, facilitating a data-driven, user-centric design selection process. The findings support the potential for real-time, neuroadaptive architectural design that mitigates decision fatigue while harmonizing objective performance metrics with affective insights. Moreover, this work contributes to research-informed public consultation by providing an evidence-based, inclusive approach for incorporating subconscious user preferences into urban planning and architectural workflows.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}