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

   https://doi.org/10.1038/s41598-021-85362-8  

   Eldeeb, Safaa; Susam, Busra T.; Akcakaya, Murat; Conner, Caitlin M.; White, Susan W.; Mazefsky, Carla A. (December 2021, Scientific Reports)

   null (Ed.)

   Abstract 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%). 

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2. Screen time as an index of family distress

   https://doi.org/10.1016/j.crbeha.2021.100023  

   Hartshorne, Joshua K.; Huang, Yi Ting; Lucio Paredes, Pablo Martín; Oppenheimer, Kathleen; Robbins, Parker T.; Velasco, María Daniela (November 2021, Current Research in Behavioral Sciences)
3. Distress among undergraduates: Marginality, stressors and resilience resources

   Nurius, P. S. (January 2021, Journal of American college health)

   null (Ed.)

   Objective: This study addresses mental health concerns among university students, examining cumulative stress exposure as well as resilience resources. Participants: Participants were 253 first- and second-year undergraduate students (age = 18.76; 49.80% male, 69% students of color) enrolled at a large western US university. Methods: Data were obtained from a cross-sectional online survey examining marginalized statuses and multiple stressors alongside coping responses, adaptive self-concept, and social support as predictors of stress, anxiety, and depression. Results: Multivariate regressions demonstrated significant associations between stress exposures and lower levels of resilience resources with each mental health indicator (with substantial R2 of.49-.60). Although stressor exposures accounted for significant increases in mental health concerns, their exploratory power was attenuated by resilience resources (e.g., beta decreases from.25 to.16). Conclusions: Better understanding cumulative adversity/resilience resource profiles, particularly among marginalized students, can help universities in prioritizing institutional support responses toward prevention and mitigating psychological distress. 

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4. Towards Noninvasive Accurate Detection of Intrapartum Fetal Hypoxic Distress

   https://doi.org/10.1109/BSN51625.2021.9507036  

   Kasap, Begum; Vali, Kourosh; Qian, Weitai; Hedriana, Herman L.; Wang, Aijun; Farmer, Diana L.; Ghiasi, Soheil (July 2021, IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN))
5. Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization

   Bargav Jayaraman, Lingxiao Wang (December 2018, Advances in neural information processing systems)

   Distributed learning allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. We present a distributed learning approach that combines differential privacy with secure multi-party computation. We explore two popular methods of differential privacy, output perturbation and gradient perturbation, and advance the state-of-the-art for both methods in the distributed learning setting. In our output perturbation method, the parties combine local models within a secure computation and then add the required differential privacy noise before revealing the model. In our gradient perturbation method, the data owners collaboratively train a global model via an iterative learning algorithm. At each iteration, the parties aggregate their local gradients within a secure computation, adding sufficient noise to ensure privacy before the gradient updates are revealed. For both methods, we show that the noise can be reduced in the multi-party setting by adding the noise inside the secure computation after aggregation, asymptotically improving upon the best previous results. Experiments on real world data sets demonstrate that our methods provide substantial utility gains for typical privacy requirements. 

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