Clinical usability of deep learning-based saliency maps for occlusion myocardial infarction identification from the prehospital 12-Lead electrocardiogram.

INTRODUCTION: Deep learning (DL) models offer improved performance in electrocardiogram (ECG)-based classification over rule-based methods. However, for widespread adoption by clinicians, explainability methods, like saliency maps, are essential. METHODS: On a subset of 100 ECGs from patients with chest pain, we generated saliency maps using a previously validated convolutional neural network for occlusion myocardial infarction (OMI) classification. Three clinicians reviewed ECG-saliency map dyads, first assessing the likelihood of OMI from standard ECGs and then evaluating clinical relevance and helpfulness of the saliency maps, as well as their confidence in the model's predictions. Questions were answered on a Likert scale ranging from +3 (most useful/relevant) to -3 (least useful/relevant). RESULTS: The adjudicated accuracy of the three clinicians matched the DL model when considering area under the receiver operating characteristics curve (AUC) and F1 score (AUC 0.855 vs. 0.872, F1 score = 0.789 vs. 0.747). On average, clinicians found saliency maps slightly clinically relevant (0.96 ± 0.92) and slightly helpful (0.66 ± 0.98) in identifying or ruling out OMI but had higher confidence in the model's predictions (1.71 ± 0.56). Clinicians noted that leads I and aVL were often emphasized, even when obvious ST changes were present in other leads. CONCLUSION: In this clinical usability study, clinicians deemed saliency maps somewhat helpful in enhancing explainability of DL-based ECG models. The spatial convolutional layers across the 12 leads in these models appear to contribute to the discrepancy between ECG segments considered most relevant by clinicians and segments that drove DL model predictions.

Pediatric Occupational Therapists' Perspectives on Sleep: A Qualitative Descriptive Study.

IMPORTANCE: Insufficient sleep is common among children seeking occupational therapy services but is rarely a focus of therapy despite sleep's critical impact on health. OBJECTIVE: To examine pediatric occupational therapists' experiences, views, and confidence in addressing sleep concerns in their practice as well as barriers to and supports for doing so. DESIGN: A qualitative descriptive study with thematic analysis of data from 1-hr virtual interviews. Rapport building, multiple-coder analysis, and member checking were used to ensure reliability and validity. SETTING: Interviews were conducted remotely at each participant's preferred time and location. PARTICIPANTS: Pediatric occupational therapists (N = 20) practicing across multiple settings in the United States were recruited through emails directed to their place of work and social media posts. A goal of 20 participants was set a priori with the goal of thematic saturation. OUTCOMES AND MEASURES: A semistructured interview guide. RESULTS: Participants were predominately cisgender (95%), female (85%), and White, non-Hispanic (90%). Overall, they voiced the importance of sleep but reported almost never writing sleep-related goals. Reported barriers that affected the participants' ability to fully address sleep in practice included therapists' lack of confidence and knowledge and low caregiver buy-in. CONCLUSIONS AND RELEVANCE: The findings identify themes on the basis of which actionable steps toward promoting occupational therapists as sleep champions can be developed. Future implications include increasing sleep education opportunities, enhancing awareness of sleep health's impact on goal areas, and facilitating discussions about occupational therapy's role within the medical system and family system in supporting sleep. Plain-Language Summary: This qualitative study identifies what helps and hinders occupational therapists in addressing the sleep health concerns of their clients. We give occupational therapy clinicians and educators key supports to seek out or barriers to address.

Geometric Analysis of Uncertainty Sampling for Dense Neural Network Layer.

For model adaptation of fully connected neural network layers, we provide an information geometric and sample behavioral active learning uncertainty sampling objective analysis. We identify conditions under which several uncertainty-based methods have the same performance and show that such conditions are more likely to appear in the early stages of learning. We define riskier samples for adaptation, and demonstrate that, as the set of labeled samples increases, margin-based sampling outperforms other uncertainty sampling methods by preferentially selecting these risky samples. We support our derivations and illustrations with experiments using Meta-Dataset, a benchmark for few-shot learning. We compare uncertainty-based active learning objectives using features produced by SimpleCNAPS (a state-of-the-art few-shot classifier) as input for a fully-connected adaptation layer. Our results indicate that margin-based uncertainty sampling achieves similar performance as other uncertainty based sampling methods with fewer labelled samples as discussed in the novel geometric analysis.

A probabilistic approach for calibration time reduction in hybrid EEG-fTCD brain-computer interfaces.

BACKGROUND: Generally, brain-computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has to attend a certain number of calibration sessions to be able to use the system. However, such calibration requirements may be difficult to fulfill especially for patients with disabilities. In this paper, we introduce a probabilistic transfer learning approach to reduce the calibration requirements of our EEG-fTCD hybrid BCI designed using motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. The proposed approach identifies the top similar datasets from previous BCI users to a small training dataset collected from a current BCI user and uses these datasets to augment the training data of the current BCI user. To achieve such an aim, EEG and fTCD feature vectors of each trial were projected into scalar scores using support vector machines. EEG and fTCD class conditional distributions were learnt separately using the scores of each class. Bhattacharyya distance was used to identify similarities between class conditional distributions obtained using training trials of the current BCI user and those obtained using trials of previous users. RESULTS: Experimental results showed that the performance obtained using the proposed transfer learning approach outperforms the performance obtained without transfer learning for both MI and flickering MR/WG paradigms. In particular, it was found that the calibration requirements can be reduced by at least 60.43% for the MI paradigm, while at most a reduction of 17.31% can be achieved for the MR/WG paradigm. CONCLUSIONS: Data collected using the MI paradigm show better generalization across subjects.

Optimal Query Selection Using Multi-Armed Bandits.

Query selection for latent variable estimation is conventionally performed by opting for observations with low noise or optimizing information theoretic objectives related to reducing the level of estimated uncertainty based on the current best estimate. In these approaches, typically the system makes a decision by leveraging the current available information about the state. However, trusting the current best estimate results in poor query selection when truth is far from the current estimate, and this negatively impacts the speed and accuracy of the latent variable estimation procedure. We introduce a novel sequential adaptive action value function for query selection using the multi-armed bandit (MAB) framework which allows us to find a tractable solution. For this adaptive-sequential query selection method, we analytically show: (i) performance improvement in the query selection for a dynamical system, (ii) the conditions where the model outperforms competitors. We also present favorable empirical assessments of the performance for this method, compared to alternative methods, both using Monte Carlo simulations and human-in-the-loop experiments with a brain computer interface (BCI) typing system where the language model provides the prior information.

Effects of Catalytic Action and Ligand Binding on Conformational Ensembles of Adenylate Kinase.

Crystal structures of adenylate kinase (AdK) from Escherichia coli capture two states: an "open" conformation (apo) obtained in the absence of ligands and a "closed" conformation in which ligands are bound. Other AdK crystal structures suggest intermediate conformations that may lie on the transition pathway between these two states. To characterize the transition from open to closed states in solution, X-ray solution scattering data were collected from AdK in the apo form and with progressively increasing concentrations of five different ligands. Scattering data from apo AdK are consistent with scattering predicted from the crystal structure of AdK in the open conformation. In contrast, data from AdK samples saturated with Ap5A do not agree with that calculated from AdK in the closed conformation. Using cluster analysis of available structures, we selected representative structures in five conformational states: open, partially open, intermediate, partially closed, and closed. We used these structures to estimate the relative abundances of these states for each experimental condition. X-ray solution scattering data obtained from AdK with AMP are dominated by scattering from AdK in the open conformation. For AdK in the presence of high concentrations of ATP and ADP, the conformational ensemble shifts to a mixture of partially open and closed states. Even when AdK is saturated with Ap5A, a significant proportion of AdK remains in a partially open conformation. These results are consistent with an induced-fit model in which the transition of AdK from an open state to a closed state is initiated by ATP binding.

An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling.

A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm design in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset characters. However, the typing accuracy and also typing speed can potentially be enhanced with more informed subset selection and flash assignment. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to presentation in each iteration, rather than showing a subset of randomly selected characters, the developed framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed. Moreover, we conduct real time experiments with human participants to study the human-in-the-loop effect on the performance of the proposed active-RBSE framework and consistent with the simulation results, the results of these experiments show improvement both in typing speed and accuracy.

Constrained Maximum Likelihood Estimation of Relative Abundances of Protein Conformation in a Heterogeneous Mixture from Small Angle X-Ray Scattering Intensity Measurements.

In this paper, we describe a model for maximum likelihood estimation (MLE) of the relative abundances of different conformations of a protein in a heterogeneous mixture from small angle X-ray scattering (SAXS) intensities. To consider cases where the solution includes intermediate or unknown conformations, we develop a subset selection method based on k-means clustering and the Cramér-Rao bound on the mixture coefficient estimation error to find a sparse basis set that represents the space spanned by the measured SAXS intensities of the known conformations of a protein. Then, using the selected basis set and the assumptions on the model for the intensity measurements, we show that the MLE model can be expressed as a constrained convex optimization problem. Employing the adenylate kinase (ADK) protein and its known conformations as an example, and using Monte Carlo simulations, we demonstrate the performance of the proposed estimation scheme. Here, although we use 45 crystallographically determined experimental structures and we could generate many more using, for instance, molecular dynamics calculations, the clustering technique indicates that the data cannot support the determination of relative abundances for more than 5 conformations. The estimation of this maximum number of conformations is intrinsic to the methodology we have used here.

Manifold Learning by Preserving Distance Orders.

Nonlinear dimensionality reduction is essential for the analysis and the interpretation of high dimensional data sets. In this manuscript, we propose a distance order preserving manifold learning algorithm that extends the basic mean-squared error cost function used mainly in multidimensional scaling (MDS)-based methods. We develop a constrained optimization problem by assuming explicit constraints on the order of distances in the low-dimensional space. In this optimization problem, as a generalization of MDS, instead of forcing a linear relationship between the distances in the high-dimensional original and low-dimensional projection space, we learn a non-decreasing relation approximated by radial basis functions. We compare the proposed method with existing manifold learning algorithms using synthetic datasets based on the commonly used residual variance and proposed percentage of violated distance orders metrics. We also perform experiments on a retinal image dataset used in Retinopathy of Prematurity (ROP) diagnosis.

AttentionCARE: replicability of a BCI for the clinical application of augmented reality-guided EEG-based attention modification for adolescents at high risk for depression.

Affect-biased attention is the phenomenon of prioritizing attention to emotionally salient stimuli and away from goal-directed stimuli. It is thought that affect-biased attention to emotional stimuli is a driving factor in the development of depression. This effect has been well-studied in adults, but research shows that this is also true during adolescence, when the severity of depressive symptoms are correlated with the magnitude of affect-biased attention to negative emotional stimuli. Prior studies have shown that trainings to modify affect-biased attention may ameliorate depression in adults, but this research has also been stymied by concerns about reliability and replicability. This study describes a clinical application of augmented reality-guided EEG-based attention modification ("AttentionCARE") for adolescents who are at highest risk for future depressive disorders (i.e., daughters of depressed mothers). Our results (n = 10) indicated that the AttentionCARE protocol can reliably and accurately provide neurofeedback about adolescent attention to negative emotional distractors that detract from attention to a primary task. Through several within and cross-study replications, our work addresses concerns about the lack of reliability and reproducibility in brain-computer interface applications, offering insights for future interventions to modify affect-biased attention in high-risk adolescents.

Evaluating the Thiessen polygon approach for efficient parameterization of urban stormwater models.

Catchment discretization plays a key role in constructing stormwater models. Traditional methods usually require aerial or topographic data to manually partition the catchment, but this approach is challenging in areas with poor data access. Here, we propose an alternative approach, by drawing Thiessen polygons around sewer nodes to construct a sewershed model. The utility of this approach is evaluated using the EPA's Storm Water Management Model (SWMM) to simulate pipe flow in a sewershed in the City of Pittsburgh. Parameter sensitivities and model uncertainties were explored via Monte Carlo simulations and a simple algorithm applied to calibrate the model. The calibrated model could reliably simulate pipe flow, with a Nash-Sutcliffe efficiency (NSE) of 0.82 when compared to measured flow. The potential influence of sewer data availability on model performance was tested as a function of the number of nodes used to build the model. No statistical differences were observed in model performance when randomly reducing the number of nodes used to build the model (up to 40%). Based on our analyses, the Thiessen polygon approach can be used to construct urban stormwater models and generate good pipe flow simulations even for sewer data limited scenarios.

Improving Lyme disease testing with data driven test design in pediatrics.

Diagnostic advances have not kept pace with the expansion of Lyme disease caused by Borrelia burgdorferi and transmitted by ticks. Lyme disease clinical manifestations can overlap with many other diagnoses making Lyme disease a critical part of many differential diagnoses in endemic areas. Current diagnostic blood tests rely on a 2-tiered algorithm for which the second step is either a time-consuming western blot or a whole cell lysate immunoassay. Neither of these second step tests allow for rapid results of this critical rule out test. We hypothesized that using western blot confirmation information, we could create computational models to propose recombinant second-tier tests that would allow for more rapid, automated, and specific testing algorithms. We propose here a framework for assessing retrospective data to determine putative recombinant assay components. A retrospective pediatric cohort of 2755 samples submitted for Lyme disease screening was assessed using support vector machine learning algorithms to optimize tier 1 diagnostic thresholds for the Vidas IgG II assay and determine optimal tier 2 components for both a positive and negative confirmation test. In cases where the tier 1 screen was negative, but clinical suspicion was high, we found that 1 protein (L58) could be used to reduce false-negative results. For second-tier testing of screen positive cases, we found that 6 proteins could be used to reduce false-positive results (L18, L39M, L39, L41, L45, and L58) with a final machine learning classifier or 2 proteins using a final rules-based approach (L41, L18). This led to an overall accuracy of 92.36% for the proposed algorithm without a final machine learning classifier and 92.12% with integration of the machine learning classifier in the final algorithm when compared to the IgG western blot as the gold-standard. Use of this framework across multiple assays and institutions will allow for a data-driven approach to assay development to provide laboratories and patients with the improvements in turnaround time needed for this testing.

Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture.

Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders. A significant barrier to usage of these signals in diagnosis and treatment is the lack of consistent relationships between GI myoelectric features and function. A potential cause of this issue is the use of arbitrary classification criteria, such as percentage of power in tachygastric and bradygastric frequency bands. Here we applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, we recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimensional residual convolutional network, followed by a fully connected layer, with a decision based on a sigmoidal output. For this 2-class problem, accuracy was 90%, sensitivity (feeding detection) was 90%, and specificity (baseline detection) was 89%. By comparison, approaches using hand-crafted features (e.g., SVM, random forest, and logistic regression) produced an accuracy from 54% to 82%, sensitivity from 46% to 84% and specificity from 66% to 80%. These results suggest that automatic feature extraction and deep neural networks could be useful to assess GI function for comparing baseline to an active functional GI state, such as feeding. In future testing, the current approach could be applied to determine normal and disease-related GI myoelectric patterns to diagnosis and assess patients with GI disease.

Characterizing rest-activity rhythms and sleep for children with and without tactile sensitivities: An observational study.

STUDY OBJECTIVES: This cross-sectional, observational study aimed to characterize and compare movement-based rest-activity rhythms (RARs) and sleep period variables of children with tactile hypersensitivities (SS) and non-sensitive peers (NSS) to expand the understanding of experienced differences in sleep. METHODS: Children (ages 6-10) wore Actigraph GT9X watches for 2 weeks and caregivers completed daily sleep diaries. RARs and sleep period variables (e.g., sleep efficiency, duration, wake after sleep onset) were analyzed and localized means were plotted to visualize average rhythms for each group. Groups were compared using Student's t tests, or non-parametric alternatives, and Hedge's g effect sizes. RESULTS: Fifty-three children and their families participated in this study (nSS = 21 nNSS = 32). The groups had similar RARs and sleep period variables. In both groups, sleep efficiency was low (SESS = 78%, SENSS = 77%) and total sleep time was short (TSTSS = 7 hrs 26 mins, TSTNSS- 7 h, 33 min) compared to national recommendations. Despite these similarities, children with SS took noticeably longer to settle down and fall asleep (53 min) than children with NSS (26 min, p = .075, g = 0.95). CONCLUSION: This study provides preliminary data describing RAR and sleep period variables in children with and without tactile hypersensitivities. While overall RAR and sleep variables were similar between groups, there is evidence that children with SS spend a longer time transitioning to sleep. Evidence is provided that wrist-worn actigraphy is tolerable and acceptable for children with tactile sensitivities. Actigraphy provides important, movement-based data that should be used in tandem with other measures of sleep health for future studies.

Feedback Related Negativity Amplitude is Greatest Following Deceptive Feedback in Autistic Adolescents.

The purpose of this study is to investigate if feedback related negativity (FRN) can capture instantaneous elevated emotional reactivity in autistic adolescents. A measurement of elevated reactivity could allow clinicians to better support autistic individuals without the need for self-reporting or verbal conveyance. The study investigated reactivity in 46 autistic adolescents (ages 12-21 years) completing the Affective Posner Task which utilizes deceptive feedback to elicit distress presented as frustration. The FRN event-related potential (ERP) served as an instantaneous quantitative neural measurement of emotional reactivity. We compared deceptive and distressing feedback to both truthful but distressing feedback and truthful and non-distressing feedback using the FRN, response times in the successive trial, and Emotion Dysregulation Inventory (EDI) reactivity scores. Results revealed that FRN values were most negative to deceptive feedback as compared to truthful non-distressing feedback. Furthermore, distressing feedback led to faster response times in the successive trial on average. Lastly, participants with higher EDI reactivity scores had more negative FRN values for non-distressing truthful feedback compared to participants with lower reactivity scores. The FRN amplitude showed changes based on both frustration and reactivity. The findings of this investigation support using the FRN to better understand emotion regulation processes for autistic adolescents in future work. Furthermore, the change in FRN based on reactivity suggests the possible need to subgroup autistic adolescents based on reactivity and adjust interventions accordingly.

Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact.

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

EEG guided electrical stimulation parameters generation from texture force profiles.

Objective.Our aim is to enhance sensory perception and spatial presence in artificial interfaces guided by EEG. This is done by developing a closed-loop electro-tactile system guided by EEG that adaptively update the electrical stimulation parameters to achieve EEG responses similar to the EEG responses generated from touching textured surface.Approach.In this work, we introduce a model that defines the relationship between the contact force profiles and the electrical stimulation parameters. This is done by using the EEG and force data collected from two experiments. The first was conducted by moving a set of textured surfaces against the subjects' fingertip, while collecting both EEG and force data. Whereas the second was carried out by applying a set of different pulse and amplitude modulated electrical stimuli to the subjects' index finger while recording EEG.Main results.We were able to develop a model which could generate electrical stimulation parameters corresponding to different textured surfaces. We showed by offline testing and validation analysis that the average error between the EEG generated from the estimated electrical stimulation parameters and the actual EEG generated from touching textured surfaces is around 7%.Significance.Haptic feedback plays a vital role in our daily life, as it allows us to become aware of our environment. Even though a number of methods have been developed to measure perception of spatial presence and provide sensory feedback in virtual reality environments, there is currently no closed-loop control of sensory stimulation. The proposed model provides an initial step towards developing a closed loop electro-tactile haptic feedback model that delivers more realistic touch sensation through electrical stimulation.

Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry.

Systems that are based on recursive Bayesian updates for classification limit the cost of evidence collection through certain stopping/termination criteria and accordingly enforce decision making. Conventionally, two termination criteria based on pre-defined thresholds over (i) the maximum of the state posterior distribution; and (ii) the state posterior uncertainty are commonly used. In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria. For example, through the proposed geometric interpretation we show that confidence thresholds defined over maximum of the state posteriors suffer from stiffness that results in unnecessary evidence collection whereas uncertainty based thresholding methods are fragile to number of categories and terminate prematurely if some state candidates are already discovered to be unfavorable. Moreover, both types of termination methods neglect the evolution of posterior updates. We then propose a new stopping/termination criterion with a geometrical insight to overcome the limitations of these conventional methods and provide a comparison in terms of decision accuracy and speed. We validate our claims using simulations and using real experimental data obtained through a brain computer interfaced typing system.

Using Neurofeedback from Steady-State Visual Evoked Potentials to Target Affect-Biased Attention in Augmented Reality.

Biases in attention to emotional stimuli (i.e., affect-biased attention) contribute to the development and mainte-nance of depression and anxiety and may be a promising target for intervention. Past attempts to therapeutically modify affect-biased attention have been unsatisfactory due to issues with reliability and precision. Electroencephalogram (EEG)-derived steady-state visual evoked potentials (SSVEPS) provide a temporally-sensitive biological index of attention to competing visual stimuli at the level of neuronal populations in the visual cortex. SSVEPS can potentially be used to quantify whether affective distractors vs. task-relevant stimuli have "won" the competition for attention at a trial-by-trial level during neuro-feedback sessions. This study piloted a protocol for a SSVEP-based neurofeedback training to modify affect-biased attention using a portable augmented-reality (AR) EEG interface. During neurofeedback sessions with five healthy participants, signifi-cantly greater attention was given to the task-relevant stimulus (a Gabor patch) than to affective distractors (negative emotional expressions) across SSVEP indices (p<0.000l). SSVEP indices exhibited excellent internal consistency as evidenced by a maximum Guttman split-half coefficient of 0.97 when comparing even to odd trials. Further testing is required, but findings suggest several SSVEP neurofeedback calculation methods most deserving of additional investigation and support ongoing efforts to develop and implement a SSVEP-guided AR-based neurofeedback training to modify affect-biased attention in adolescent girls at high risk for depression.