Point-of-Care Potassium Measurement vs Artificial Intelligence-Enabled Electrocardiography for Hyperkalemia Detection.

Background: Hyperkalemia can be detected by point-of-care (POC) blood testing and by artificial intelligence- enabled electrocardiography (ECG). These 2 methods of detecting hyperkalemia have not been compared.

Objective: To determine the accuracy of POC and ECG potassium measurements for hyperkalemia detection in patients with critical illness.

The usability of a wireless vocal fold vibration switch for individuals with complex communication needs: a caregiver-inclusive iterative home-based evaluation.

A vocal fold vibration switch is a type of access technology that detects voluntary vibrations of the vocal cords. In two sequential usability studies, we evaluated successive prototypes of a novel wireless vocal fold vibration switch. Each usability study enroled 7 dyads consisting of individuals with complex communication needs and their caregivers.

Case report: Novel use of clinical brain-computer interfaces in recreation programming for an autistic adolescent with co-occurring attention deficit hyperactivity disorder.

Background: In recent years, several autistic children and youth have shown interest in Holland Bloorview Kids Rehabilitation Hospital's clinical brain computer interface (BCI) program. Existing literature about BCI use among autistic individuals has focused solely on cognitive skill development and remediation of challenging behaviors. To date, the benefits of recreational BCI programming with autistic children and youth have not been documented.

Children with developmental coordination disorder display atypical interhemispheric connectivity during conscious and subconscious rhythmic auditory-motor synchronization.

Children with developmental coordination disorder (DCD) display difficulties in perception-action coupling when engaging in tasks requiring predictive timing. We investigated the influence of awareness on auditory-motor adjustments to small and large rhythmic perturbations in the auditory sequence to examine whether children synchronize their movements automatically or through planning and whether those adjustments occur consciously or subconsciously. Electroencephalography (EEG) was used to assess functional connectivity patterns underlying different adjustment strategies.

Physiotherapy-assisted overground exoskeleton use: mixed methods feasibility study protocol quantifying the user experience, as well as functional, neural, and muscular outcomes in children with mobility impairments.

Background: Early phase research suggests that physiotherapy paired with use of robotic walking aids provides a novel opportunity for children with severe mobility challenges to experience active walking. The Trexo Plus is a pediatric lower limb exoskeleton mounted on a wheeled walker frame, and is adjustable to fit a child's positional and gait requirements. It guides and powers the child's leg movements in a way that is individualized to their movement potential and upright support needs, and can provide progressive challenges for walking within a physiotherapy-based motor learning treatment paradigm.

Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction.

Background: Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction.

Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs.

While electroencephalography (EEG)-based brain-computer interfaces (BCIs) have many potential clinical applications, their use is impeded by poor performance for many users. To improve BCI performance, either via enhanced signal processing or user training, it is critical to understand and describe each user's ability to perform mental control tasks and produce discernible EEG patterns. While classification accuracy has predominantly been used to assess user performance, limitations and criticisms of this approach have emerged, thus prompting the need to develop novel user assessment approaches with greater descriptive capability.

Corrigendum: Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.

[This corrects the article DOI: 10.3389/fncom.2023.

Online Ternary Classification of Covert Speech by Leveraging the Passive Perception of Speech.

Brain-computer interfaces (BCIs) provide communicative alternatives to those without functional speech. Covert speech (CS)-based BCIs enable communication simply by thinking of words and thus have intuitive appeal. However, an elusive barrier to their clinical translation is the collection of voluminous examples of high-quality CS signals, as iteratively rehearsing words for long durations is mentally fatiguing.

Electroencephalographic interbrain synchronization in children with disabilities, their parents, and neurologic music therapists.

As with typically developing children, children with cerebral palsy and autism spectrum disorder develop important socio-emotional rapport with their parents and healthcare providers. However, the neural mechanisms underlying these relationships have been less studied. By simultaneously measuring the brain activity of multiple individuals, interbrain synchronization could serve as a neurophysiological marker of social-emotional responses.

Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.

Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity.

A pediatric near-infrared spectroscopy brain-computer interface based on the detection of emotional valence.

Brain-computer interfaces (BCIs) are being investigated as an access pathway to communication for individuals with physical disabilities, as the technology obviates the need for voluntary motor control. However, to date, minimal research has investigated the use of BCIs for children. Traditional BCI communication paradigms may be suboptimal given that children with physical disabilities may face delays in cognitive development and acquisition of literacy skills.

Artificial intelligence-enabled electrocardiography identifies severe dyscalcemias and has prognostic value.

Context: Abnormal serum calcium concentrations affect the heart and may alter the electrocardiogram (ECG), but the detection of hypocalcemia and hypercalcemia (collectively dyscalcemia) relies on blood laboratory tests requiring turnaround time.

Objective: The study aimed to develop a bloodless artificial intelligence (AI)-enabled (ECG) method to rapidly detect dyscalcemia and analyze its possible utility for outcome prediction.

Methods: This study collected 86,731 development, 15,611 tuning, 11,105 internal validation, and 8401 external validation ECGs from electronic medical records with at least 1 ECG associated with an albumin-adjusted calcium (aCa) value within 4 h.

Recent Trends in Non-invasive Neural Recording Based Brain-to-Brain Synchrony Analysis on Multidisciplinary Human Interactions for Understanding Brain Dynamics: A Systematic Review.

The study of brain-to-brain synchrony has a burgeoning application in the brain-computer interface (BCI) research, offering valuable insights into the neural underpinnings of interacting human brains using numerous neural recording technologies. The area allows exploring the commonality of brain dynamics by evaluating the neural synchronization among a group of people performing a specified task. The growing number of publications on brain-to-brain synchrony inspired the authors to conduct a systematic review using the PRISMA protocol so that future researchers can get a comprehensive understanding of the paradigms, methodologies, translational algorithms, and challenges in the area of brain-to-brain synchrony research.

Does music induce interbrain synchronization between a non-speaking youth with cerebral palsy (CP), a parent, and a neurologic music therapist? A brief report.

Shared emotional experiences during musical activities among musicians can be coupled with brainwave synchronization. For non-speaking individuals with CP, verbal communication may be limited in expressing mutual empathy. Therefore, this case study explored interbrain synchronization among a non-speaking CP (female, 18 yrs), her parent, and a music therapist by measuring their brainwaves simultaneously during four music and four storytelling sessions.

Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction.

Dyskalemias are common electrolyte disorders associated with high cardiovascular risk. Artificial intelligence (AI)-assisted electrocardiography (ECG) has been evaluated as an early-detection approach for dyskalemia. The aims of this study were to determine the clinical accuracy of AI-assisted ECG for dyskalemia and prognostic ability on clinical outcomes such as all-cause mortality, hospitalizations, and ED revisits.

A comparison and classification of oscillatory characteristics in speech perception and covert speech.

Covert speech, the mental imagery of speaking, has been studied increasingly to understand and decode thoughts in the context of brain-computer interfaces. In studies of speech comprehension, neural oscillations are thought to play a key role in the temporal encoding of speech. However, little is known about the role of oscillations in covert speech.

Investigating sensory response to physical discomfort in children with autism spectrum disorder using near-infrared spectroscopy.

Self-reporting of pain can be difficult in populations with communication challenges or atypical sensory processing, such as children with autism spectrum disorder (ASD). Consequently, pain can go untreated. An objective method to identify discomfort would be valuable to individuals unable to express or recognize their own bodily distress.

Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review.

Brain-computer interfaces (BCIs) represent a new frontier in the effort to maximize the ability of individuals with profound motor impairments to interact and communicate. While much literature points to BCIs' promise as an alternative access pathway, there have historically been few applications involving children and young adults with severe physical disabilities. As research is emerging in this sphere, this article aims to evaluate the current state of translating BCIs to the pediatric population.

Artificial Intelligence-Assisted Electrocardiography for Early Diagnosis of Thyrotoxic Periodic Paralysis.

Context: Thyrotoxic periodic paralysis (TPP) characterized by acute weakness, hypokalemia, and hyperthyroidism is a medical emergency with a great challenge in early diagnosis since most TPP patients do not have overt symptoms.

Objective: This work aims to assess artificial intelligence (AI)-assisted electrocardiography (ECG) combined with routine laboratory data in the early diagnosis of TPP.

Methods: A deep learning model (DLM) based on ECG12Net, an 82-layer convolutional neural network, was constructed to detect hypokalemia and hyperthyroidism.

Neurophysiological Synchrony Between Children With Severe Physical Disabilities and Their Parents During Music Therapy.

Although physiological synchronization has been associated with the level of empathy in emotionally meaningful relationships, little is known about the interbrain synchrony between non-speaking children with severe disabilities and their familial caregivers. In a repeated measures observational study, we ascertained the degree of interbrain synchrony during music therapy in 10 child-parent dyads, where the children were non-speaking and living with severe motor impairments. Interbrain synchrony was quantified via measurements of spectral coherence and Granger causality between child and parent electroencephalographic (EEG) signals collected during ten 15-min music therapy sessions per dyad, where parents were present as non-participating, covert observers.

Automated movement recognition to predict motor impairment in high-risk infants: a systematic review of diagnostic test accuracy and meta-analysis.

Aim: To assess the sensitivity and specificity of automated movement recognition in predicting motor impairment in high-risk infants.

Method: We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Scopus databases and identified additional studies from the references of relevant studies. We included studies that evaluated automated movement recognition in high-risk infants to predict motor impairment, including cerebral palsy (CP) and non-CP motor impairments.

Advancing Brain-Computer Interface Applications for Severely Disabled Children Through a Multidisciplinary National Network: Summary of the Inaugural Pediatric BCI Canada Meeting.

Thousands of youth suffering from acquired brain injury or other early-life neurological disease live, mature, and learn with only limited communication and interaction with their world. Such cognitively capable children are ideal candidates for brain-computer interfaces (BCI). While BCI systems are rapidly evolving, a fundamental gap exists between technological innovators and the patients and families who stand to benefit.

Customized Access Technology for Children using Head Movement Recognition.

Children with cerebral palsy and complex communication needs face limitations in their access technology (AT) usage. Speech recognition software and conventional ATs (e.g.