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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise.

Mindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is challenging given the reliance on self-report tools. Electroencephalography (EEG) has successfully quantified neural responses to emotional arousal and meditation in other populations, making it ideal to objectively measure neural responses before and after mindfulness (MF) practice among individuals with ASD. We performed an EEG-based analysis during a resting state paradigm in 35 youth with ASD. Specifically, we developed a machine learning classifier and a feature and channel selection approach that separates resting states preceding (Pre-MF) and following (Post-MF) a mindfulness meditation exercise within participants. Across individuals, frontal and temporal channels were most informative. Total power in the beta band (16-30 Hz), Total power (4-30 Hz), relative power in alpha band (8-12 Hz) were the most informative EEG features. A classifier using a non-linear combination of selected EEG features from selected channel locations separated Pre-MF and Post-MF resting states with an average accuracy, sensitivity, and specificity of 80.76%, 78.24%, and 82.14% respectively. Finally, we validated that separation between Pre-MF and Post-MF is due to the MF prime rather than linear-temporal drift. This work underscores machine learning as a critical tool for separating distinct resting states within youth with ASD and will enable better classification of underlying neural responses following brief MF meditation.

Detection of Stroke-Induced Visual Neglect and Target Response Prediction Using Augmented Reality and Electroencephalography.

We aim to build a system incorporating electroencephalography (EEG) and augmented reality (AR) that is capable of identifying the presence of visual spatial neglect (SN) and mapping the estimated neglected visual field. An EEG-based brain-computer interface (BCI) was used to identify those spatiospectral features that best detect participants with SN among stroke survivors using their EEG responses to ipsilesional and contralesional visual stimuli. Frontal-central delta and alpha, frontal-parietal theta, Fp1 beta, and left frontal gamma were found to be important features for neglect detection. Additionally, temporal analysis of the responses shows that the proposed model is accurate in detecting potentially neglected targets. These targets were predicted using common spatial patterns as the feature extraction algorithm and regularized discriminant analysis combined with kernel density estimation for classification. With our preliminary results, our system shows promise for reliably detecting the presence of SN and predicting visual target responses in stroke patients with SN.

Characterizing Sleep Differences in Children With and Without Sensory Sensitivities.

Objectives: Individuals register and react to daily sensory stimuli differently, which influences participation in occupations. Sleep is a foundational nightly occupation that impacts overall health and development in children. Emerging research suggests that certain sensory processing patterns, specifically sensory sensitivities, may have a negative impact on sleep health in children. In this study, we aimed to (i) characterize sleep in children with and without sensory sensitivities and (ii) examine the relationship between sensory processing patterns (using the Sensory Profile-2) and sleep using validated parent- and child-reported questionnaires. We hypothesized that children with sensory sensitivities will exhibit more difficulties with sleep. Methods: We recruited 22 children (ages 6-10) with sensory sensitivities (SS) and 33 children without sensory sensitivities (NSS) to complete validated sleep and sensory processing questionnaires: the Children's Sleep Habits Questionnaire (CSHQ), Sleep Self-Report (SSR), and Sensory Profile-2. Results: Children with SS had significantly more sleep behaviors reported by both parents (p < 0.001, g = 1.11) and children (p < 0.001, g = 1.17) compared to children with NSS. Specifically, children with SS had higher frequencies of sleep anxiety (p = 0.004, g = 0.79), bedtime resistance (p = 0.001, g = 0.83), and sleep onset delay (p = 0.003, g = 0.95). Spearman's ρ correlations indicated significant positive correlations between parent- and child-reported sleep. Children with SS showed a larger association and greater variability between sleep and sensory processing compared to their peers. Significant positive correlations between parent-reported sleep behaviors and sensory sensitive and avoiding patterns were identified for both children with SS and NSS. Child-reported sleep behaviors were most strongly associated with sensitive and avoiding patterns for children with NSS and seeking patterns for children with SS. Conclusion: We present evidence that sleep is impacted for children with SS to a greater extent than children with NSS. We also identified that a child's sensory processing pattern may be an important contributor to sleep problems in children with and without sensory sensitivities. Sleep concerns should be addressed within routine care for children with sensory sensitivities. Future studies will inform specific sleep intervention targets most salient for children with SS and other sensory processing patterns.

Automated Pain Assessment in Children Using Electrodermal Activity and Video Data Fusion via Machine Learning.

OBJECTIVE: Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary and deliberate. The presented approach supplements the subjective self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions to develop an objective pain assessment metric. Such an approach is specifically important for assessing pain in children who are not capable of providing accurate self-pain reports, requiring nonverbal pain assessment. We demonstrate the performance of our approach using data recorded from children in post-operative recovery following laparoscopic appendectomy. We examined separately and combined the usefulness of EDA and video facial expression data as predictors of children's self-reports of pain following surgery through recovery. Findings indicate that EDA and facial expression data independently provide above chance sensitivities and specificities, but their fusion for classifying clinically significant pain vs. clinically nonsignificant pain achieved substantial improvement, yielding 90.91% accuracy, with 100% sensitivity and 81.82% specificity. The multimodal measures capitalize upon different features of the complex pain response. Thus, this paper presents both evidence for the utility of a weighted maximum likelihood algorithm as a novel feature selection method for EDA and video facial expression data and an accurate and objective automated classification algorithm capable ofdiscriminating clinically significant pain from clinically nonsignificant pain in children.

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.

Fine-tuning and Personalization of EEG-based Neglect Detection in Stroke Patients.

Spatial neglect (SN) is a neurological disorder that causes inattention to visual stimuli in the contralesional visual field, stemming from unilateral brain injury such as stroke. The current gold standard method of SN assessment, the conventional Behavioral Inattention Test (BIT-C), is highly variable and inconsistent in its results. In our previous work, we built an augmented reality (AR)-based BCI to overcome the limitations of the BIT-C and classified between neglected and non-neglected targets with high accuracy. Our previous approach included personalization of the neglect detection classifier but the process required rigorous retraining from scratch and time-consuming feature selection for each participant. Future steps of our work will require rapid personalization of the neglect classifier; therefore, in this paper, we investigate fine-tuning of a neural network model to hasten the personalization process.

Machine Learning Classification of False-Positive Human Immunodeficiency Virus Screening Results.

BACKGROUND: Human immunodeficiency virus (HIV) screening has improved significantly in the past decade as we have implemented tests that include antigen detection of p24. Incorporation of p24 detection narrows the window from 4 to 2 weeks between infection acquisition and ability to detect infection, reducing unintentional spread of HIV. The fourth- and fifth-generation HIV (HIV5G) screening tests in low prevalence populations have high numbers of false-positive screens and it is unclear if orthogonal testing improves diagnostic and public health outcomes. METHODS: We used a cohort of 60,587 HIV5G screening tests with molecular and clinical correlates collected from 2016 to 2018 and applied machine learning to generate a classifier that could predict likely true and false positivity. RESULTS: The best classification was achieved by using support vector machines and transformation of results with principle component analysis. The final classifier had an accuracy of 94% for correct classification of false-positive screens and an accuracy of 92% for classification of true-positive screens. CONCLUSIONS: Implementation of this classifier as a screening method for all HIV5G reactive screens allows for improved workflow with likely true positives reported immediately to reduce infection spread and initiate follow-up testing and treatment and likely false positives undergoing orthogonal testing utilizing the same specimen already drawn to reduce distress and follow-up visits. Application of machine learning to the clinical laboratory allows for workflow improvement and decision support to provide improved patient care and public health.

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.

EEG-based texture roughness classification in active tactile exploration with invariant representation learning networks.

During daily activities, humans use their hands to grasp surrounding objects and perceive sensory information which are also employed for perceptual and motor goals. Multiple cortical brain regions are known to be responsible for sensory recognition, perception and motor execution during sensorimotor processing. While various research studies particularly focus on the domain of human sensorimotor control, the relation and processing between motor execution and sensory processing is not yet fully understood. Main goal of our work is to discriminate textured surfaces varying in their roughness levels during active tactile exploration using simultaneously recorded electroencephalogram (EEG) data, while minimizing the variance of distinct motor exploration movement patterns. We perform an experimental study with eight healthy participants who were instructed to use the tip of their dominant hand index finger while rubbing or tapping three different textured surfaces with varying levels of roughness. We use an adversarial invariant representation learning neural network architecture that performs EEG-based classification of different textured surfaces, while simultaneously minimizing the discriminability of motor movement conditions (i.e., rub or tap). Results show that the proposed approach can discriminate between three different textured surfaces with accuracies up to 70%, while suppressing movement related variability from learned representations.

Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).