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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.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.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. The main outcomes were to assess the accuracy of AI-ECG to predict aCa and follow up these patients for all-cause mortality, new-onset acute myocardial infraction (AMI), and new-onset heart failure (HF) to validate the ability of AI-ECG-aCa for previvor identification.ECG-aCa had mean absolute errors (MAE) of 0.78/0.98 mg/dL and achieved an area under receiver operating characteristic curves (AUCs) 0.9219/0.8447 and 0.8948/0.7723 to detect severe hypercalcemia and hypocalcemia in the internal/external validation sets, respectively. Although 20 % variance of ECG-aCa could be explained by traditional ECG features, the ECG-aCa was found to be associated with more complications. Patients with ECG-hypercalcemia but initially normal aCa were found to have a higher risk of subsequent all-cause mortality [hazard ratio (HR): 2.05, 95 % conference interval (CI): 1.55-2.70], new-onset AMI (HR: 2.88, 95 % CI: 1.72-4.83), and new-onset HF (HR: 2.02, 95 % CI: 1.38-2.97) in the internal validation set, which were also seen in external validation.The AI-ECG-aCa may help detecting severe dyscalcemia for early diagnosis and ECG-hypercalcemia also has prognostic value for clinical outcomes (all-cause mortality and new-onset AMI and HF).

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. In only the youth-parent dyad, we observed a significantly higher level of interbrain synchronization during music rather than story-telling condition. However, in both the youth-parent and youth-therapist dyad, regardless of condition type, significant interbrain synchronization emerged in frontal and temporal lobes in the low-frequency bands, which are associated with socio-emotional responses. Although interbrain synchronization may have been induced by multiple factors (e.g., external stimuli, shared empathetic experiences, and internal physiological rhythms), the music activity setting deserves further study as a potential facilitator of neurophysiological synchrony between youth with CP and caregivers/healthcare providers.

This study evaluated the change in usability, mental workload and information transfer rate associated with an augmentative and alternative communication (AAC) interface designed through ecological interface design (EID). The design and development process is detailed in Shea et al. (2021) . Digital AAC interfaces are considered high-tech interventions for individuals who experience complex communication needs (e.g., from etiologies such as cerebral palsy) and enable users to select language options from a visual display. Interface usability, mental workload and information transfer rate collectively influence users’ communication. Ten AAC-naïve participants engaged in three semi–scripted conversations (verbal, AAC-mediated commercial interface, and EID interfaces) with an actor. Augmentative and alternative communication interfaces were accessed through a single switch pathway. Information transfer rate, error rate, heart rate variability and subjective workload performance measures were recorded for every trial. During AAC-mediated trials, interface interactions were also documented. The EID AAC interface presented improved communication in 5 out of 7 performance measures ( p < .05). The EID AAC interface was associated with a significantly higher information transfer rate, lower error rate, less time elapsed between switch activations, less switch activation per word communicated and lower subjective workload. The application of EID to an AAC interface can lead to a significantly improved communication experience.

BackgroundBrain-computer interfaces (BCIs) can offer solutions to communicative impairments induced by conditions such as locked-in syndrome. While covert speech-based BCIs have garnered interest, a major issue facing their clinical translation is the collection of sufficient volumes of high signal-to-noise ratio (SNR) examples of covert speech signals which can typically induce fatigue in users. Fortuitously, investigations into the linkage between covert speech and speech perception have revealed spatiotemporal similarities suggestive of shared encoding mechanisms. Here, we sought to demonstrate that an electroencephalographic cross-condition machine learning model of speech perception and covert speech can successfully decode neural speech patterns during online BCI scenarios.MethodsIn the current study, ten participants underwent a dyadic protocol whereby participants perceived the audio of a randomly chosen word and then subsequently mentally rehearsed it. Eight words were used during the offline sessions and subsequently narrowed down to three classes for the online session (two words, rest). The modelling was achieved by estimating a functional mapping derived from speech perception and covert speech signals of the same speech token (features were extracted via a Riemannian approach).ResultsWhile most covert speech BCIs deal with binary and offline classifications, we report an average ternary and online BCI accuracy of 75.3% (60% chance-level), reaching up to 93% in select participants. Moreover, we found that perception-covert modelling effectively enhanced the SNR of covert speech signals correlatively to their high-frequency correspondences.ConclusionsThese findings may pave the way to efficient and more user-friendly data collection for passively training such BCIs. Future iterations of this BCI can lead to a combination of audiobooks and unsupervised learning to train a non-trivial vocabulary that can support proto-naturalistic communication.Significance StatementCovert speech brain-computer interfaces (BCIs) provide new communication channels. However, these BCIs face practical challenges in collecting large volumes of high-quality covert speech data which can both induce fatigue and degrade BCI performance. This study leverages the reported spatiotemporal correspondences between covert speech and speech perception by deriving a functional mapping between them. While multiclass and online covert speech classification has previously been challenging, this study reports an average ternary and online classification accuracy of 75.3%, reaching up to 93% for select participants. Moreover, the current modelling approach augmented the signal-to-noise ratio of covert speech signals correlatively to their gamma-band correspondences. The proposed approach may pave the way toward a more efficient and user-friendly method of training covert speech BCIs.

Autism spectrum disorder, cerebral palsy, brain stem stroke, and neurological injury are examples of conditions that may limit vocal communication. Augmentative and alternative communication (AAC) systems can provide a communication pathway to users who experience such complex communication needs, facilitating their societal participation and supporting some ability to direct their own care. We adapted the cognitive work analysis (CWA) framework to a linguistic domain for insights into an AAC design that best supports users’ communication. First, we applied the work domain analysis (WDA) to a popular commercial AAC system, Proloquo2Go. Data were gathered from guided AAC system use, domain experts, and the syntactic rules of the English language. The WDA exposed unmet needs in the commercial system. We then applied worker competency analysis to consider different approaches to present information and support user actions. The design included graphic forms and process views, and their integration into viewports and the workspace. Our novel application of CWA uncovered new considerations in AAC interface design and presents a nascent area of investigation, namely, AAC displays that more effectively support users’ goals. Future investigation will evaluate the mental workload of this AAC interface compared to that of current commercially available systems.

Background Recently, a novel single-switch access technology based on infrared thermography was proposed. The technology exploits the temperature differences between the inside and surrounding areas of the mouth as a switch trigger, thereby allowing voluntary switch activation upon mouth opening. However, for this technology to be clinically viable, it must be validated against a gold standard switch, such as a chin switch, that taps into the same voluntary motion. Methods In this study, we report an experiment designed to gauge the concurrent validity of the infrared thermal switch. Ten able-bodied adults participated in a series of 3 test sessions where they simultaneously used both an infrared thermal and conventional chin switch to perform multiple trials of a number identification task with visual, auditory and audiovisual stimuli. Participants also provided qualitative feedback about switch use. User performance with the two switches was quantified using an efficiency measure based on mutual information. Results User performance (p = 0.16) and response time (p = 0.25) with the infrared thermal switch were comparable to those of the gold standard. Users reported preference for the infrared thermal switch given its non-contact nature and robustness to changes in user posture. Conclusions Thermal infrared access technology appears to be a valid single switch alternative for individuals with disabilities who retain voluntary mouth opening and closing.

Background People with severe motor impairments often require an alternative access pathway, such as a binary switch, to communicate and to interact with their environment. A wide range of access pathways have been developed from simple mechanical switches to sophisticated physiological ones. In this manuscript we report the inaugural investigation of infrared thermography as a non-invasive and non-contact access pathway by which individuals with disabilities can interact and perhaps eventually communicate. Methods Our method exploits the local temperature changes associated with mouth opening/closing to enable a highly sensitive and specific binary switch. Ten participants (two with severe disabilities) provided examples of mouth opening and closing. Thermographic videos of each participant were recorded with an infrared thermal camera and processed using a computerized algorithm. The algorithm detected a mouth open-close pattern using a combination of adaptive thermal intensity filtering, motion tracking and morphological analysis. Results High detection sensitivity and low error rate were achieved for the majority of the participants (mean sensitivity of all participants: 88.5% ± 11.3; mean specificity of all participants: 99.4% ± 0.7). The algorithm performance was robust against participant motion and changes in the background scene. Conclusion Our findings suggest that further research on the infrared thermographic access pathway is warranted. Flexible camera location, convenience of use and robustness to ambient lighting levels, changes in background scene and extraneous body movements make this a potential new access modality that can be used night or day in unconstrained environments.

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. Instead, in this study we explored emotional state as an alternative access pathway to communication. We developed a pediatric BCI to identify positive and negative emotional states from changes in hemodynamic activity of the prefrontal cortex (PFC). To train and test the BCI, 10 neurotypical children aged 8–14 underwent a series of emotion-induction trials over four experimental sessions (one offline, three online) while their brain activity was measured with functional near-infrared spectroscopy (fNIRS). Visual neurofeedback was used to assist participants in regulating their emotional states and modulating their hemodynamic activity in response to the affective stimuli. Child-specific linear discriminant classifiers were trained on cumulatively available data from previous sessions and adaptively updated throughout each session. Average online valence classification exceeded chance across participants by the last two online sessions (with 7 and 8 of the 10 participants performing better than chance, respectively, in Sessions 3 and 4). There was a small significant positive correlation with online BCI performance and age, suggesting older participants were more successful at regulating their emotional state and/or brain activity. Variability was seen across participants in regards to BCI performance, hemodynamic response, and discriminatory features and channels. Retrospective offline analyses yielded accuracies comparable to those reported in adult affective BCI studies using fNIRS. Affective fNIRS-BCIs appear to be feasible for school-aged children, but to further gauge the practical potential of this type of BCI, replication with more training sessions, larger sample sizes, and end-users with disabilities is necessary.

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. Two authors independently assessed studies for inclusion, extracted data, and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies-2. Meta-analyses were performed using hierarchical summary receiver operating characteristic models. Results: Of 6536 articles, 13 articles assessing 59 movement variables in 1248 infants under 5months corrected age were included. Of these, 143 infants had CP. The overall sensitivity and specificity for motor impairment were 0.73 (95% confidence interval [CI] 0.68–0.77) and 0.70 (95% CI 0.65–0.75) respectively. Comparatively, clinical General Movements Assessment (GMA) was found to have sensitivity and specificity of 98% (95% CI 74–100) and 91% (95% CI 83–93) respectively. Sensor-based technologies had higher specificity (0.88, 95% CI 0.80–0.93). Interpretation: Automated movement recognition technology remains inferior to clinical GMA. The strength of this study is its meta-analysis to summarize performance, although generalizability of these results is limited by study heterogeneity.

A concussion is known as a functional injury affecting brain communication, integration, and processing. There is a need to objectively measure how concussions disrupt brain activation while completing ecologically relevant tasks.The objective of this study was to compare brain activation patterns between concussion and comparison groups (non-concussed youth) during a cognitive-motor single and dual-task paradigm utilizing functional near-infrared spectroscopy (fNIRS) in regions of the frontal-parietal attention network and compared to task performance.Youth with concussion generally exhibited hyperactivation and recruitment of additional brain regions in the dorsal lateral prefrontal (DLPFC), superior (SPC) and inferior parietal cortices (IPC), which are associated with processing, information integration, and response selection. Additionally, hyper- or hypo-activation patterns were associated with slower processing speed on the cognitive task. Our findings corroborate the growing literature suggesting that neural recovery may be delayed compared to the restoration of behavioral performance post-concussion.Concussion, near-infrared spectroscopy, dual-task paradigm, cognitive, motor, brain activation.

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. This retrospective cohort study was done at two hospitals within a health system from May 2019 to December 2020. In total, 26,499 patients with 34,803 emergency department (ED) visits to an academic medical center and 6492 ED visits from 4747 patients to a community hospital who had a 12-lead ECG to estimate ECG-K+ and serum laboratory potassium measurement (Lab-K+) within 1 h were included. ECG-K+ had mean absolute errors (MAEs) of ≤0.365 mmol/L. Area under receiver operating characteristic curves for ECG-K+ to predict moderate-to-severe hypokalemia (Lab-K+ ≤3 mmol/L) and moderate-to-severe hyperkalemia (Lab-K+ ≥ 6 mmol/L) were >0.85 and >0.95, respectively. The U-shaped relationships between K+ concentration and adverse outcomes were more prominent for ECG-K+ than for Lab-K+. ECG-K+ and Lab-K+ hyperkalemia were associated with high HRs for 30-day all-cause mortality. Compared to hypokalemic Lab-K+, patients with hypokalemic ECG-K+ had significantly higher risk for adverse outcomes after full confounder adjustment. In addition, patients with normal Lab-K+ but dyskalemic ECG-K+ (pseudo-positive) also exhibited more co-morbidities and had worse outcomes. Point-of-care bloodless AI ECG-K+ not only rapidly identified potentially severe hypo- and hyperkalemia, but also may serve as a biomarker for medical complexity and an independent predictor for adverse outcomes.

Augmentative and alternative communication (AAC) systems enable communication for individuals with complex communication needs. The frequency users rely on the communication systems necessitates an interface design that supports usability. As a first step to systematically designing a user interface, the Work Domain Analysis (WDA) framework was applied to a popular commercial AAC system, Proloquo2Go, to analyze and understand system constraints and design a new user interface for an AAC system. For the purpose of the analysis, the system boundaries were that of verbal language-based communication. Information for the analysis of the system was gathered through exploratory operation of AAC systems and structured interviews with a speech-language pathologist and parents of AAC system users. Research into the process of language formation showed logical groupings of the different types of language components to communicate information regarding states, objects, relationships, and emotions; component types were subsequently grouped for high-level communication of ideas and requests. An abstraction hierarchy was developed to represent the various levels of abstraction of the system from which information requirements were determined. This paper presents a novel application of WDA for AAC systems. Results derived from the application of WDA have yielded valuable design considerations for an AAC system user.

There is increasing interest in developing intuitive brain-computer interfaces (BCIs) to differentiate intuitive mental tasks such as imagined speech. Both electroencephalography (EEG) and function...

Cough is the forceful and rapid expulsion of air that clears the airway of foreign material, fluid or mucus, and may be symptomatic of respiratory conditions and swallowing difficulties. The evaluation of cough severity can thus inform appropriate treatment of the underlying issue, but assessment has historically been subjective (e.g., self-report) and episodic. Automatic cough detection has been proposed as a tool for long-term (e.g., nocturnal) monitoring of cough intensity and frequency, but the rejection of non-cough activities remains an elusive challenge. Cervical accelerometry is a novel tool, which measures oropharyngeal and laryngopharyngeal vibrations associated with swallowing, coughing, tongue movements and speech. We propose an automatic cough detection system that discriminates accelerometry signals associated with coughs from those representing swallows, tongue movements and speech. We consider both voluntary and reflexive coughs. Accelerometry signals were represented in term of time-frequency meta features. Using the binary genetic feature selection algorithm and a support vector machine classifier, the proposed system achieved a cough detection accuracy of 99.26 ± 0.12% when discriminating between voluntary cough and rest accelerometry signals. An accuracy of 90 ± 13.9% was achieved using elastic net feature selection and support vector machine when classifying involuntary coughs and rest signals. When discriminating between voluntary and involuntary cough versus all other non-cough artefacts, the proposed system achieved accuracies of 90.2 ± 3.6% and 80.3 ± 10.5%, respectively. Compared to current cough monitoring systems which require combinations of microphones, accelerometers, and video recorders, the proposed method requires only a single accelerometer.

One method for improving the accuracy and hence the rate of communication of a brain–computer interface (BCI) is to automatically correct erroneous classifications by exploiting error-related poten...

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. The development cohort consisted of 39 ECGs from patients with TPP and 502 ECGs of hypokalemic controls; the validation cohort consisted of 11 ECGs of TPP patients and 36 ECGs of non-TPP individuals with weakness. The AI-ECG–based TPP diagnostic process was then consecutively evaluated in 22 male patients with TTP-like features. Results In the validation cohort, the DLM-based ECG system detected all cases of hypokalemia in TPP patients with a mean absolute error of 0.26 mEq/L and diagnosed TPP with an area under curve (AUC) of approximately 80%, surpassing the best standard ECG parameter (AUC = 0.7285 for the QR interval). Combining the AI predictions with the estimated glomerular filtration rate and serum chloride boosted the diagnostic accuracy of the algorithm to AUC 0.986. In the prospective study, the integrated AI and routine laboratory diagnostic system had a PPV of 100% and F-measure of 87.5%. Conclusion An AI-ECG system reliably identifies hypokalemia in patients with paralysis, and integration with routine blood chemistries provides valuable decision support for the early diagnosis of TPP.

Functional communication is indispensable for child development at all times but during this COVID-19, non-verbal children become more anxious about social distancing and self-quarantine due to sudden aberration on daily designed practices and professional support. These verbally challenged children require the support of Augmentative and Alternative Communication (AAC) for intercommunication. Therefore, during COVID-19, assistance must be provided remotely to these users by a AAC team involving caregivers, teachers, Speech Language Therapist (SLT) to ensure collaborative learning and development of non-verbal child communication skills. However, most of the advanced AAC, such as Speech Generating Devices (SGD), Picture Exchange Communication System (PECS) based mobile applications (Android & iOS) are designed considering the scenario of developed countries and less accessible in developing countries. Therefore, in this study, we are focusing on representing feasible short term strategies, prospective challenges and as long term strategy, a cloud based framework entitled as “Bolte Chai+”, which is an intelligent integrated collaborative learning platform for non-verbal children, parents, caregivers, teachers and SLT. The intelligent analytics within the platform monitors child overall progress by tracking child activity in mobile application and conversely support parents and AAC team to concentrate on individual child ubiquitous abilities. We believe, the proposed framework and strategies will empower non-verbal children and assist researchers, policy makers to acknowledge a definitive solution to implement AAC as communication support in developing countries during COVID-19 pandemic.

Augmentative and Alternative Communication (AAC) technology research not only enhance communication but also communal skills in verbally challenged children with Autism Spectrum Disorder (ASD). However, the research on AAC technology is mainly concentrated in developed countries and less explored in developing countries. In this study, we utilized evaluation method to analyze the prospects, existing practices and future possibilities, benefits and barriers of AAC research in developing countries. It is found that Speech Generating Devices (SGD) are mostly preferred by children and in future artificial intelligence (AI) based mobile application will augment communication skills among verbally challenged children. We conclude with general recommendation on succeeding research, collaborative approach and implementation with funding opportunities for substantial growth of AAC technology research in developing countries. This study will facilitate directions for initiating AAC research in developing countries and will accommodate researchers, developers and stakeholders to acknowledge opportunities, barriers, probable and current state of AAC research.

Event-related potentials (ERPs) have previously been used to confirm the existence of the fast optical signal (FOS) but validation methods have mainly been limited to exploring the temporal correspondence of FOS peaks to those of ERPs. The purpose of this study was to systematically quantify the relationship between FOS and ERP responses to a visual oddball task in both time and frequency domains. Near-infrared spectroscopy (NIRS) and electroencephalography (EEG) sensors were co-located over the prefrontal cortex while participants performed a visual oddball task. Fifteen participants completed 2 data collection sessions each, where they were instructed to keep a mental count of oddball images. The oddball condition produced a positive ERP at 200 ms followed by a negativity 300–500 ms after image onset in the frontal electrodes. In contrast to previous FOS studies, a FOS response was identified only in DC intensity signals and not in phase delay signals. A decrease in DC intensity was found 150–250 ms after oddball image onset with a 400-trial average in 10 of 15 participants. The latency of the positive 200 ms ERP and the FOS DC intensity decrease were significantly correlated for only 6 (out of 15) participants due to the low signal-to-noise ratio of the FOS response. Coherence values between the FOS and ERP oddball responses were found to be significant in the 3–5 Hz frequency band for 10 participants. A significant Granger causal influence of the ERP on the FOS oddball response was uncovered in the 2–6 Hz frequency band for 7 participants. Collectively, our findings suggest that, for a majority of participants, the ERP and the DC intensity signal of the FOS are spectrally coherent, specifically in narrow frequency bands previously associated with event-related oscillations in the prefrontal cortex. However, these electro-optical relationships were only found in a subset of participants. Further research on enhancing the quality of the event-related FOS signal is required before it can be practically exploited in applications such as brain-computer interfacing.

Although previous studies have highlighted both similarities and differences between the timing of electromyography (EMG) and mechanomyography (MMG) activities of muscles, there is no method to systematically quantify the temporal alignment between corresponding EMG and MMG signals. We proposed a novel method to determine the level of coincident activity in quasi-periodic MMG and EMG signals. The method optimizes 3 muscle-specific parameters: amplitude threshold, window size and minimum percent of EMG and MMG overlap using a particle swarm optimization algorithm to maximize the agreement (balanced accuracy) between electrical and mechanical muscle activity. The method was applied to bilaterally recorded EMG and MMG signals from 4 lower limb muscles per side of 25 pediatric participants during self-paced gait. Mean balanced accuracy exceeded 75% for all muscles except the lateral gastrocnemius, where EMG and MMG misalignment was notable (56% balanced accuracy). The proposed method can be applied to the criterion-driven comparison of simultaneously recorded myographic signals from two different measurement modalities during a motor task.

Objectives Brain-computer interfaces (BCIs) allow users to operate a device or application by means of cognitive activity. This technology will ultimately be used in real-world environments which include the presence of distractors. The purpose of the study was to determine the effect of visual distractors on BCI performance. Methods Sixteen able-bodied participants underwent neurofeedback training to achieve motor imagery-guided BCI control in an online paradigm using electroencephalography (EEG) to measure neural signals. Participants then completed two sessions of the motor imagery EEG-BCI protocol in the presence of infrequent, small visual distractors. BCI performance was determined based on classification accuracy. Results The presence of distractors was found to affect motor imagery-specific patterns in mu and beta power. However, the distractors did not significantly affect the BCI classification accuracy; across participants, the mean classification accuracy was 81.5 ± 14% for non-distractor trials, and 78.3 ± 17% for distractor trials. Conclusion This minimal consequence suggests that the BCI was robust to distractor effects, despite motor imagery-related brain activity being attenuated amid distractors. Significance A BCI system that mitigates distraction-related effects may improve the ease of its use and ultimately facilitate the effective translation of the technology from the lab to the home.

Purpose: Existing speech generating devices (SGD) often require caregiver intervention for setup and positioning, and thus limit opportunities for spontaneous social interaction. The advent of cond...

Communication using brain–computer interfaces (BCIs) can be non-intuitive, often requiring the performance of a conversation-irrelevant task such as hand motor imagery. In this paper, the reliability of electroencephalography (EEG) signals in discriminating between different covert speech tasks is investigated. Twelve participants, across two sessions each, were asked to perform multiple iterations of three differing mental tasks for 10 s each: unconstrained rest or the mental repetition of the words “yes” or “no.” A multilayer perceptron (MLP) artificial neural network (ANN) was used to classify all three pairwise combinations of “yes,” “no,” and rest trials and also for ternary classification. An average accuracy of 75.7% ± 9.6 was reached in the classification of covert speech trials versus rest, with all participants exceeding chance level (57.8%). The classification of “yes” versus “no” yielded an average accuracy of 63.2 ± 6.4 with ten participants surpassing chance level (57.8%). Finally, the ternary classification yielded an average accuracy of 54.1% ± 9.7 with all participants exceeding chance level (39.1%). The proposed MLP network provided significantly higher accuracies compared to some of the most common classification techniques in BCI. To our knowledge, this is the first report of using ANN for the classification of EEG covert speech across multiple sessions. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.

Conductive thread-based sensors have the potential to offer ambulatory measurement of body movement and posture. While such sensors have been previously described in terms of their overall correlation with existing measurement modalities, the ability of thread-based sensors to predict linear elongation has yet to be investigated. We constructed multiple stitched sensors, 20 cm in length, using two silver-coated nylon threads and seven different types of stitches, including cover and overlock stitches. Stitched sensors underwent elongation and cyclic stress tests while sensor resistance was recorded. Optimal predictive functions were derived for each sensor such that the range-normalized error was minimized. Responses were found to be governed by the combination of stitch geometry and physical properties of the threads. The average error in predicting elongation was 14% of the usable sensor range, suggesting that the stitched sensors are perhaps better suited to intermittent detection of stretched versus unstretched states rather than continuous prediction of absolute elongation. Nonetheless, coverstitches were generally superior to overlock stitches in terms of minimizing range-normalized error. The path of the conductive looper thread and the presence of stabilizing needle threads were important determinants of the sensor’s prediction capability and repeatability. Stress tests revealed a systematic upward drift of the resistance response curve for all sensors over repeated trials, suggesting that these stitched sensors require adequate recovery time between measurements. Given the possibility of applying stitched sensors non-destructively to existing garments, further study of these sensors—particularly those comprising multiple independent stitches in novel stitch geometries—remains worthwhile.

Electromyography (EMG) is the standard modality for measuring muscle activity. However, the convenience and availability of low-cost accelerometer-based wearables makes mechanomyography (MMG) an increasingly attractive alternative modality for clinical applications. Literature to date has demonstrated a strong association between EMG and MMG temporal alignment in isometric and isokinetic contractions. However, the EMG-MMG relationship has not been studied in gait. In this study, the concurrence of EMG- and MMG-detected contractions in the tibialis anterior, lateral gastrocnemius, vastus lateralis, and biceps femoris muscles were investigated in children during self-paced gait. Furthermore, the distribution of signal power over the gait cycle was statistically compared between EMG-MMG modalities. With EMG as the reference, muscular contractions were detected based on MMG with balanced accuracies between 88 and 94% for all muscles except the gastrocnemius. MMG signal power differed from that of EMG during certain phases of the gait cycle in all muscles except the biceps femoris. These timing and power distribution differences between the two modalities may in part be related to muscle fascicle length changes that are unique to muscle motion during gait. Our findings suggest that the relationship between EMG and MMG appears to be more complex during gait than in isometric and isokinetic contractions.

For non-verbal individuals, brain-computer interfaces (BCIs) are a potential means of communication. Near-infrared spectroscopy (NIRS) is a brain-monitoring modality that has been considered for BCIs. To date, limited NIRS-BCI testing has involved online classification, particularly with individuals with severe motor impairments.We tested an online NIRS-BCI developed for a non-verbal individual with severe congenital motor impairments. The binary BCI differentiated categorical verbal fluency task (VFT) performance and rest using prefrontal measurements. The participant attended five sessions, the last two of which were online with classification feedback.An online classification accuracy of 63.33% was achieved using a linear discriminant classifier trained on a four-dimensional feature set. An offline, cross-validation analysis of all data yielded an optimal adjusted classification accuracy of 66.6 ± 9.11%. Inconsistent functional responses, contradictory effects of feedback, participant fatigue and motion artefacts were identified as challenges to online classification specific to this participant.Results suggest potential in using an NIRS-BCI controlled by the VFT in instances of severe congenital impairments. Further testing with users with severe disabilities is necessary. Implications for Rehabilitation Brain-computer interfaces (BCIs) can provide a non-motor based means of communication for individuals with severe motor impairments. Near-infrared spectroscopy (NIRS) is a haemodynamic-based brain-imaging modality used in BCIs. To date, NIRS-BCIs have not been thoroughly tested with potential target users. This case study shows that NIRS-BCIs may offer a means of practical communication for individuals with severe congenital impairments and continued exploration is advisable.

Background Uncovering the correct diagnosis of chronic hypokalemia with potassium (K+) wasting from the kidneys or gut can be fraught with challenges. We identified clinical and laboratory parameters helpful for differentiating the causes of chronic hypokalemia. Methods Normotensive patients referred to our tertiary academic medical center for the evaluation of chronic hypokalemia were prospectively enrolled over 5 years. Clinical features, laboratory examinations-including blood and spot urine electrolytes, acid-base status, biochemistries, and hormones-as well as genetic analysis, were determined. Results Ninety-nine patients with chronic normotensive hypokalemia (serum K+ 2.8 ± 0.4 mmol/L, duration 4.1 ± 0.9 years) were enrolled. Neuromuscular symptoms were the most common complaints. Although Gitelman syndrome (n = 33), Bartter syndrome (n = 10), and distal renal tubular acidosis (n = 12) were the predominant renal tubular disorders, 44 patients (44%) were diagnosed with anorexia/bulimia nervosa (n = 21), surreptitious use of laxatives (n = 11), or diuretics (n = 12). Patients with gastrointestinal causes and surreptitious diuretics use exhibited a female predominance, lower body mass index, and less K+ supplementation. High urine K+ excretion (transtubular potassium gradient >3, urine K+/Cr >2 mmol/mmol) was universally present in patients with renal tubular disorders, but also found in >50% patients with gastrointestinal causes. Of interest, while urine sodium (Na+) and chloride (Cl-) excretions were high and coupled (urine Na+/Cl- ratio ∼1) in renal tubular disorders and "on" diuretics use, skewed or uncoupled urine Na+ and Cl- excretions were found in anorexia/bulimia nervosa and laxatives abuse (urine Na+/Cl- ratio: 5.0 ± 2.2, 0.4 ± 0.2, respectively) and low urine Na+ and Cl- excretions with fixed Na+/Cl- ratios (0.9 ± 0.2) when "off" diuretics. Conclusion Besides body mass index, sex, and blood acid-base status, integrated interpretation of the urine Na+:Cl- excretion and their ratio is important to make an accurate diagnosis and treatment plan for patients with chronic normotensive hypokalemia.

Background Emerging evidence suggests that autonomic nervous system (ANS) function is affected in ASD. Existing literature has focused on quantifying the activity of ANS components (i.e. sympathetic/parasympathetic systems) individually. To this end, measures such as respiratory sinus arrhythmia (RSA) and high- and low-frequency spectral components of heart rate variability have been employed. Recent findings indicate that these methods employ assumptions that do not fully consider the complexity of the ANS system and the interaction of its components. To this end, we propose a new method for examining ANS function in ASD. Methods A sample of typically developing (TD) children ( n = 34) and children with ASD ( n = 45) performed the Reading the Mind in the Eyes Task (RMET), preceded and followed by periods of baseline movie-watching. Multiscale Entropy (MSE) was employed as a new tool to examine the complexity of the cardiac signal. Results Significant group differences in MSE emerged to suggest atypicality in the regularity of the autonomic signal at short and long time scales in the ASD group during the RMET task. Conclusions For the RMET task, our results may suggest atypical activation of parasympathetic influences on the heart, which contributes to short-term patterns in the heartbeat signal, as well as atypicalities in the slower acting processes. Results also suggest that the coupling between the fast-acting and slow-acting influences may be atypical in ASD during the examined social cognition task.

PurposeThe purpose of this paper is to describe how an Academic Health Science Centre, providing pediatric rehabilitation services, research, and education, developed a Centres for Leadership (CfL) initiative to integrate its academic functions and embrace the goal of being a learning organization.Design/methodology/approachHistorical documents, tracked output information, and staff members’ insights were used to describe the ten-year evolution of the initiative, its benefits, and transformational learnings for the organization.FindingsThe evolutions concerned development of a series of CfLs, and changes over time in leadership and management structure, as well as in operations and targeted activities. Benefits included enhanced clinician engagement in research, practice-based research, and impacts on clinical practice. Transformational learnings concerned the importance of supporting stakeholder engagement, fostering a spirit of inquiry, and fostering leaderful practice. These learnings contributed to three related emergent outcomes reflecting “way stations” on the journey to enhanced evidence-informed decision making and clinical excellence: enhancements in authentic partnerships, greater innovation capacity, and greater understanding and actualization of leadership values.Practical implicationsPractical information is provided for other organizations interested in understanding how this initiative evolved, its tangible value, and its wider benefits for organizational collaboration, innovation, and leadership values. Challenges encountered and main messages for other organizations are also considered.Originality/valueA strategy map is used to present the structures, processes, and outcomes arising from the initiative, with the goal of informing the operations of other organizations desiring to be learning organizations.

Traditional brain-computer interfaces often exhibit unstable performance over time. It has recently been proposed that passive brain-computer interfaces may provide a way to complement and stabilize these traditional systems. In this study, we investigated the feasibility of a passive brain-computer interface that uses electroencephalography to monitor changes in mental state on a single-trial basis. We recorded cortical activity from 15 locations while 11 able-bodied adults completed a series of challenging mental tasks. Using a feature clustering algorithm to account for redundancy in EEG signal features, we classified self-reported changes in fatigue, frustration, and attention levels with 74.8 ± 9.1%, 71.6 ± 5.6%, and 84.8 ± 7.4% accuracy, respectively. Based on the most frequently-selected features across all participants, we note the importance of the frontal and central electrodes for fatigue detection, posterior alpha band and frontal beta band activity for frustration detection, and posterior alpha band activity for attention detection. Future work will focus on integrating these results with an active brain-computer interface.

Augmentative and Alternative Communication (AAC) emerged as a combination of methods or strategies that constitute any device, such as Speech Generating Device (SGD), Program (mobile applications), Procedure (PECS, Picture Exchange Communication System), which enhances individual’s communication ability. Autism Spectrum Disorder (ASD) is a spectrum of comprehensive neurodevelopment disorder that leads to speech impairments, repetitive behavior, and social communication difficulties; therefore, it is imperative to underscore that at the core of all impediments are communication impairment. This article represents a systematic review of research initiatives that investigate multi-modal AAC strategies and functionality, features of mobile applications to reinforce communication and communal skills in verbally challenged ASD children because other researches are focused only on low or high-tech AAC or interventions to provide insights on ASD children respond to a particular approach. Following the PRISMA method, a total of 60 (January 2015 to October 2020) research articles were reviewed, indexed by Springer, Science Direct, Scopus, ACM, IEEE databases, and published in the AAC journal. The selected research articles are categorized into different themes where most of them focused on interactive mobile applications to improve emotional, social, learning, and overall communication skills in verbally challenged ASD children. This systematic review provides an outline of the paradigm shift in AAC modalities from PECS to Artificial Intelligence (AI), Machine Learning (ML), and Augmented Reality (AR) based applications. It opens up underline future opportunities to integrate intelligent analytics features in mobile applications to strengthen communication skills in verbally undermined ASD children.

Dual-axis swallowing accelerometry has been proposed as a method for quantifying swallowing function. Acceleration signals in anterior–posterior and superior–inferior anatomical directions are processed and automatically segmented. However, the latter is often too liberal, admitting pre- and post-swallowing activity while also giving rise to non-swallow segments. These segmentation shortcomings adversely affect feature extraction and ultimately classification of swallowing function. In this paper, we propose a kernel density estimation-based algorithm to adaptively trim the swallow segments, and energy and noise-floor algorithms to reduce the number of false positive swallow segments. The balance between false positive reduction and loss of true positives can be adjusted according to algorithmic thresholds. Dramatic reductions ( $-$ 85.4%) in false positives can be achieved with a moderate loss of true positives ( $-$ 15.1%).

Brain computer interfaces (BCI) can provide communication opportunities for individuals with severe motor disabilities. Transcranial Doppler ultrasound (TCD) measures cerebral blood flow velocities and can be used to develop a BCI. A previously implemented TCD BCI system used verbal and spatial tasks as control signals; however, the spatial task involved a visual cue that awkwardly diverted the user's attention away from the communication interface. Therefore, vision-independent right-lateralized tasks were investigated. Using a bilateral TCD BCI, ten participants controlled online, an on-screen keyboard using a left-lateralized task (verbal fluency), a right-lateralized task (fist motor imagery or 3D-shape tracing), and unconstrained rest. 3D-shape tracing was generally more discernible from other tasks than was fist motor imagery. Verbal fluency, 3D-shape tracing and unconstrained rest were distinguished from each other using a linear discriminant classifier, achieving a mean agreement of κ=0.43±0.17. These rates are comparable to the best offline three-class TCD BCI accuracies reported thus far. The online communication system achieved a mean information transfer rate (ITR) of 1.08±0.69bits/min with values reaching up to 2.46bits/min, thereby exceeding the ITR of previous online TCD BCIs. These findings demonstrate the potential of a three-class online TCD BCI that does not require visual task cues.

The error-related potential (ErrP) can inform the correction of brain-computer interface (BCI) mistakes, but it has thus far been incorporated only into visual BCIs, with mixed success. Given that ErrPs are thought to have higher impact when BCI accuracy is relatively low, we sought to identify the aurally evoked ErrP and investigate its auto-corrective value in auditory BCIs, which typically yield lower accuracies than visual BCIs. We implemented an auditory P300 BCI with four selectable items. Each of nine typically developed participants attempted to spell letter sequences on two separate days. Erroneous feedback was detected by (i) making use of the ErrP, (ii) assessing BCI selection confidence, and (iii) combining these two pieces of information into a hybrid detector. ErrPs were detected with an average cross-validation area under the curve of 0.946. Simulated automatic correction by reverting to the second-ranked letter improved participant-wise information transfer rate by 2.3 bits/minute when err...

Background The analysis of electroencephalograms is often performed in the frequency-domain. These analyses typically involve the computation of spectral power either over pre-defined frequency bands (e.g. theta, delta, alpha, and beta bands) or over a large number of narrow frequency ranges. However, the former technique ignores variability in these frequency bands over time and between participants while the latter ignores the significant redundancy between these powers. New method This paper details an unsupervised feature extraction method for EEG data that uses a clustering of features to agglomerate narrow-band spectral powers based on their similarities. This method computes a set of analogues to the traditional frequency bands that are data-driven and participant-specific. A fast correlation-based filter was used to identify which of these agglomerated features were most useful for each investigated classification problem. Results The new feature clustering algorithm was used to detect changes in three mental states and to detect the performance of three mental tasks. Balanced classification accuracies approaching or exceeding 70% were attained for all classification problems. Comparison to existing methods Classification accuracies attained by this algorithm were compared to those attained by two frequency-domain algorithms that did not employ clustering – a wide-band algorithm based on the spectral power within the theta, delta, alpha, and beta bands and a narrow-band algorithm based on the spectral power within 1-Hz ranges. Overall, the feature clustering algorithm was statistically superior to both alternative algorithms. Conclusions The new feature clustering algorithm provides a promising alternative to conventional frequency-domain EEG analysis.

Brain–computer interfaces (BCIs) can provide a means of communication to individuals with severe motor disorders, such as those presenting as locked-in. Many BCI paradigms rely on motor neural pathways, which are often impaired in these individuals. However, recent findings suggest that visuospatial function may remain intact. This study aimed to determine whether visuospatial imagery, a previously unexplored task, could be used to signify intent in an online electroencephalography (EEG)-based BCI. Eighteen typically developed participants imagined checkerboard arrow stimuli in four quadrants of the visual field in 5-s trials, while signals were collected using 16 dry electrodes over the visual cortex. In online blocks, participants received graded visual feedback based on their performance. An initial BCI pipeline (visuospatial imagery classifier I) attained a mean accuracy of [Formula: see text]% classifying rest against visuospatial imagery in online trials. This BCI pipeline was further improved using restriction to alpha band features (visuospatial imagery classifier II), resulting in a mean pseudo-online accuracy of [Formula: see text]%. Accuracies exceeded the threshold for practical BCIs in 12 participants. This study supports the use of visuospatial imagery as a real-time, binary EEG-BCI control paradigm.

A brain-computer interface (BCI) is a system that translates neural activity into a practical output. Its functionality, therefore, depends not only on the computer itself, but also on the cognitive system of the user. Distractors have the potential to capture attention, increase cognitive load, and may therefore impact BCI use. The purpose of the current study is to determine the effects of small visual distractors on the cognitive load of users of a motor imagery-BCI, and to examine whether these distractor-mediated effects can be improved by modifying the task interface. Sixteen typically-developed participants completed two sessions of online motor imagery to control an EEG-BCI, under conditions of no distractors, visual distractors, and cognitive strategies (intended to mitigate cognitive load) amid distractors. Cognitive load for each session was assessed through both a ratio of theta to alpha power and the NASA-Task Load Index (NASA-TLX). Task-irrelevant visual stimuli were found to significantly increase the objective measure of cognitive load, particularly for parietal channels. Subjective cognitive load as indexed by the NASA-TLX was predictive of a decrease in BCI performance for participants with below 0.75 classification accuracy (R2 = 0.32, p

The diagnosis of cerebral palsy (CP) is difficult before 2 years of age. The general movements assessment (GMA) is a method for predicting CP from the spontaneous movements of infants in the first months of life. This assessment has shown high accuracy in predicting CP, but its use is limited by a lack of trained clinicians and its subjective nature. An objective and cost-effective alternative is the automatic videobased assessment of infant movements. Retrospective videos with clinical GMA outcomes were evaluated against eligibility criteria for the automatic analysis consisting of a skin model for segmentation and large displacement optical flow (LDOF) for motion tracking. Kinematic features were extracted to classify the movements as typical or atypical using different classification algorithms. Preliminary classification results obtained from the analysis of 127 videos of preterm infants showed up to 92% of accuracy in predicting CP. A computerbased assessment would provide clinicians with an objective tool for early diagnosis of CP, to facilitate early intervention and improve functional outcomes.

OBJECTIVE Most brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS) require that users perform mental tasks such as motor imagery, mental arithmetic, or music imagery to convey a message or to answer simple yes or no questions. These cognitive tasks usually have no direct association with the communicative intent, which makes them difficult for users to perform. APPROACH In this paper, a 3-class intuitive BCI is presented which enables users to directly answer yes or no questions by covertly rehearsing the word 'yes' or 'no' for 15 s. The BCI also admits an equivalent duration of unconstrained rest which constitutes the third discernable task. Twelve participants each completed one offline block and six online blocks over the course of two sessions. The mean value of the change in oxygenated hemoglobin concentration during a trial was calculated for each channel and used to train a regularized linear discriminant analysis (RLDA) classifier. MAIN RESULTS By the final online block, nine out of 12 participants were performing above chance (p

Evidence suggests that the cerebral blood flow patterns accompanying cognitive activity are retained in many locked-in patients. These patterns can be monitored using transcranial Doppler ultrasound (TCD), a medical imaging technique that measures bilateral cerebral blood flow velocities. Recently, TCD has been proposed as an alternative imaging modality for brain–computer interfaces (BCIs). However, most previous TCD-BCI studies have performed offline analyses with impractically lengthy tasks. In this study, we designed a BCI that automatically differentiates between counting and verbal fluency tasks using sequential hypothesis testing to make decisions as quickly as possible. Ten able-bodied participants silently alternated between counting and verbal fluency tasks within the paradigm of a simulated on-screen keyboard. During this experiment, blood flow velocities were recorded within the left and right middle cerebral arteries using bilateral TCD. Twelve features were used to characterize TCD signals. In a simulated online analysis, sequential hypothesis testing was used to update estimates of class probability every 250 ms as TCD data were processed. Classification was terminated once a threshold level of certainty was reached. Mean classification accuracy across all participants was 72% after an average of 23 s, compared to an offline analysis which obtained a classification accuracy of 80% after 45 s. This represents a substantial gain in data transmission rate, while maintaining classification accuracies exceeding 70%. Furthermore, a range of decision times between 19 and 28 s was observed, suggesting that the ability of sequential hypothesis testing to adapt the task duration for each individual participant is critical to achieving consistent performance across participants. These results indicate that sequential hypothesis testing is a promising alternative for online TCD-BCIs.

Background Near-infrared spectroscopy (NIRS) brain–computer interfaces (BCIs) enable users to interact with their environment using only cognitive activities. This paper presents the results of a comparison of four methodological frameworks used to select a pair of tasks to control a binary NIRS-BCI; specifically, three novel personalized task paradigms and the state-of-the-art prescribed task framework were explored. New methods Three types of personalized task selection approaches were compared, including: user-selected mental tasks using weighted slope scores (WS-scores), user-selected mental tasks using pair-wise accuracy rankings (PWAR), and researcher-selected mental tasks using PWAR. These paradigms, along with the state-of-the-art prescribed mental task framework, where mental tasks are selected based on the most commonly used tasks in literature, were tested by ten able-bodied participants who took part in five NIRS-BCI sessions. Results The frameworks were compared in terms of their accuracy, perceived ease-of-use, computational time, user preference, and length of training. Most notably, researcher-selected personalized tasks resulted in significantly higher accuracies, while user-selected personalized tasks resulted in significantly higher perceived ease-of-use. It was also concluded that PWAR minimized the amount of data that needed to be collected; while, WS-scores maximized user satisfaction and minimized computational time. Comparison with existing method In comparison to the state-of-the-art prescribed mental tasks, our findings show that overall, personalized tasks appear to be superior to prescribed tasks with respect to accuracy and perceived ease-of-use. Conclusions The deployment of personalized rather than prescribed mental tasks ought to be considered and further investigated in future NIRS-BCI studies.

Anxiety is one of the most concerning comorbidities in autism spectrum disorder (ASD) due to its high prevalence, negative impact on physical and psychological well-being, and interaction with core deficits of ASD. Current assessment and treatment of anxiety, which rely on the observation of behavior and self-reports, are often ineffective as ASD is associated with deficits in communication and diminished introspective ability. In this light, autonomic nervous system (ANS) activity has been suggested as a marker of physiological arousal associated with anxiety. However, physiological arousal measured by ANS indices also occurs with other cognitive and emotional processes, and it is unclear whether anxiety-related arousal can be differentiated from that related to other cognitive processes. To address this gap, we investigated the use of linear and nonlinear classification techniques for differentiating anxiety-related arousal from arousal due to three cognitive processes (attention, inhibitory control, and social cognition) and physical activity based on electrocardiography signal features. Our results indicate that over 80% classification accuracy can be achieved, suggesting that ANS response can be used as a specific marker of anxiety-related arousal in a subgroup of children with ASD who demonstrate an increase in heart rate in response to anxiogenic stimuli.

Near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have primarily made use of measurements taken from a single cortical area. In particular, the anterior prefrontal cortex has been the key area used for detecting higher-level cognitive task performance. However, mental task execution typically requires coordination between several, spatially-distributed brain regions. We investigated the value of expanding the area of interrogation to include NIRS measurements from both the prefrontal and parietal cortices to decode mental states. Hemodynamic activity was monitored at 46 locations over the prefrontal and parietal cortices using a continuous-wave near-infrared spectrometer while 11 able-bodied adults rested or performed either the verbal fluency task (VFT) or Stroop task. Offline classification was performed for the three possible binary problems using 25 iterations of bagging with a linear discriminant base classifier. Classifiers were trained on a 10 dimensional feature set. When all 46 measurement locations were considered for classification, average accuracies of 80.4±7.0%, 82.4±7.6%, and 82.8±5.9% in differentiating VFT vs rest, Stroop vs rest and VFT vs Stroop, respectively, were obtained. Relative to using measurements from the anterior PFC alone, an overall average improvement of 11.3% was achieved. Utilizing NIRS measurements from the prefrontal and parietal cortices can be of value in classifying mental states involving working memory and attention. NIRS-BCI accuracies may be improved by incorporating measurements from several, distinct cortical regions, rather than a single area alone. Further development of an NIRS-BCI supporting combinations of VFT, Stroop task and rest states is also warranted.

This study further delineates the merits and limitations of the Access Technology Delivery Protocol (ATDP) through its application to two children with severe disabilities.We conducted mixed methods case studies to demonstrate the ATDP with two children with no reliable means of access to an external device. Evaluations of response efficiency, satisfaction, goal attainment, technology use and participation were made after 8 and 16 weeks of training with custom access technologies.After 16 weeks, one child's switch offered improved response efficiency, high teacher satisfaction and increased participation. The other child's switch resulted in improved satisfaction and switch effectiveness but lower overall efficiency. The latter child was no longer using his switch by the end of the study.These contrasting findings indicate that changes to any contextual factors that may impact the user's switch performance should mandate a reassessment of the access pathway. Secondly, it is important to ensure that individuals who will be responsible for switch training be identified at the outset and engaged throughout the ATDP. Finally, the ATDP should continue to be tested with individuals with severe disabilities to build an evidence base for the delivery of response efficient access solutions. Implications for Rehabilitation A data-driven, comprehensive access technology delivery protocol for children with complex communication needs could help to mitigate technology abandonment. Successful adoption of an access technology requires personalized design, training of the technology user, the teaching staff, the caregivers and other communication partners, and integration with functional activities.

As a noninvasive and safe optical measure of hemodynamic brain activity, near-infrared spectroscopy (NIRS) has emerged as a potential brain-computer interface (BCI) access modality. Currently, to the best of our knowledge, all NIRS BCIs use mental tasks to elicit changes in regional hemodynamic activity. One of the limitations of using mental tasks is that they can be cognitively demanding, and unintuitive. The goal of this work was to explore the development of a neurofeedback-based NIRS BCI that weans users off mental tasks, to instead use voluntary self-regulation. Ten able-bodied participants were recruited for this study. After ten sessions of using two personalized mental tasks to increase and decrease the participant’s hemodynamic activity, the users were asked, for the remaining sessions, to stop performing their tasks and instead use only a desire to modulate their hemodynamic activity. By the final online session, participants were able to exclusively use voluntary self-regulation with an average accuracy of $79 \pm 13\hbox{\%}$ . Additionally, the majority of participants indicated that BCI control via self-regulation was less taxing and more intuitive than BCI operation using mental tasks.

Dual-axis accelerometry has recently shown promise as a non-invasive method for detecting swallowing impairment using signal processing and pattern classification algorithms. However, it is unknown whether variations in sensor placement alter signal characteristics, threatening the accuracy of signal processing classifiers for aspiration detection. To address this question, water swallows were recorded in 14 healthy adults using a dual-axis accelerometer in 13 different positions (baseline, and 2, 4, 6 and 8 mm above, below and to the right of baseline). The baseline position was midline, immediately below the thyroid cartilage during quiet breathing. After segmentation and pre-processing, signal features were extracted in multiple domains (time, frequency, time-frequency). The effect of sensor position on signal feature distributions was examined with non-parametric statistical analysis. The analysis showed that the sensor could be displaced by as much as 4 mm inferior and lateral to the baseline position and by up to 6 mm above the baseline location without significantly altering time-frequency features. In other words, when considering the baseline position as the origin, the admissible region for sensor placement spans 10 mm in the superior-inferior axis and 8 mm in the medial-lateral direction. Results of this study suggest that time-frequency representations of accelerometry signals are most robust to sensor placement variations around the baseline position. The implication of this finding is that a swallowing accelerometry classifier based on time-frequency features can likely tolerate small variations in sensor location without degradation in classification performance.

Electronic textiles have potential for many practical uses in clinical rehabilitation. This scoping review appraises recent and emerging developments of textile-based sensors with applications to rehabilitation. Contributions published from 2009 to 2013 are appraised with a specific focus on the measured physiological or biomechanical phenomenon, current measurement practices, textile innovations, and their merits and limitations. While fabric-based signal quality and sensor integration have advanced considerably, overall system integration (including circuitry and power) has not been fully realized. Validation against clinical gold standards is inconsistent at best, and feasibility with clinical populations remains to be demonstrated. The overwhelming focus of research and development has been on remote sensing but the opportunity for textile-mediated feedback to the wearer remains unexplored. Recommendations for future research are provided.