Member, Maternal & Child Health Research Institute (MCHRI)
Honors & Awards
Jump Start Award for Excellence in Research, Stanford University (2020)
Postdoctoral Fellowship from the Center for Automotive Research, Stanford University (2018)
Postdoctoral Fellowship, Norwegian Centres of Expertise (2017)
Predoctoral Fellowship from the Scandinavian Consortium for Organizational Research, Stanford University (2015)
Predoctoral Fellowship from the Vice Dean of Education, Norwegian University of Science and Technology (2014)
Thesis award, Best Master’s Thesis in Automotive Engineering in the Academic year 2012/2013, RWTH Aachen University (2013)
Boards, Advisory Committees, Professional Organizations
Founding Director, Stanford Women Empowerment Initiative (2022 - Present)
Peer Review Committee Member, NSF (2020 - Present)
Women Empowerment Coach, Stanford Grant Writing Academy (2021 - Present)
Human Factors Specialist, NATO (2019 - Present)
Postdoc, Stanford University, School of Medicine, Interaction Neuroscience (2021)
Postdoc, Stanford University, School of Medicine, Precision Health (2019)
Visiting Researcher, Stanford University, Computer Science, Human Computer Interaction (2017)
Visiting Researcher, Stanford University, Center for Design Research, Human Machine Interaction (2017)
Ph.D., NTNU, Mechanical Engineering (2017)
M.Eng., RWTH Aachen University, Mechanical Engineering and Business Administration (2012)
B.Eng., RWTH Aachen University, Mechanical Engineering and Business Administration (2008)
Functional near-infrared spectroscopy brain imaging predicts symptom severity in youth exposed to traumatic stress.
Journal of psychiatric research
2021; 144: 494-502
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique with the potential to enable the assessment of posttraumatic stress disorder (PTSD) brain biomarkers in an affordable and portable manner. Consistent with biological models of PTSD, functional magnetic resonance imaging (fMRI) and fNIRS studies of adults with trauma exposure and PTSD symptoms suggest increased activation in the dorsolateral prefrontal cortex (dlPFC) and ventrolateral PFC (vlPFC) in response to negative emotion stimuli. We tested this theory with fNIRS assessment among youth exposed to traumatic stress and experiencing PTSD symptoms (PTSS). A portable fNIRS system collected hemodynamic responses from (N=57) youth with PTSS when engaging in a classic emotion expression task that included fearful and neutral faces stimuli. The General Linear Model was applied to identify cortical activations associated with the facial stimuli. Subsequently, a prediction model was established via a Support Vector Regression to determine whether PTSS severity could be predicted based on fNIRS-derived cortical response measures and individual demographic information. Results were consistent with findings from adult fMRI and fNIRS studies of PTSS showing increased activation in the dlPFC and vlPFC in response to negative emotion stimuli. Subsequent prediction analysis revealed ten features (i.e., cortical responses from eight frontocortical fNIRS channels, age and sex) strongly correlated with PTSS severity (r=0.65, p<.001). Our findings suggest the potential utility of fNIRS as a portable tool for the detection of putative PTSS brain biomarkers.
View details for DOI 10.1016/j.jpsychires.2021.10.020
View details for PubMedID 34768071
Individualized stress detection using an unmodified car steering wheel.
2021; 11 (1): 20646
In-car passive stress sensing could enable the monitoring of stress biomarkers while driving and reach millions of commuters daily (i.e., 123 million daily commuters in the US alone). Here, we present a nonintrusive method to detect stress solely from steering angle data of a regular car. The method uses inverse filtering to convert angular movement data into a biomechanical Mass Spring Damper model of the arm and extracts its damped natural frequency as an approximation of muscle stiffness, which in turn reflects stress. We ran a within-subject study (N=22), in which commuters drove a vehicle around a closed circuit in both stress and calm conditions. As hypothesized, cohort analysis revealed a significantly higher damped natural frequency for the stress condition (P=.023, d=0.723). Subsequent automation of the method achieved rapid (i.e., within 8 turns) stress detection in the individual with a detection accuracy of 77%.
View details for DOI 10.1038/s41598-021-00062-7
View details for PubMedID 34667184
Dynamic Inter-Brain Synchrony in Real-life Inter-Personal Cooperation: A Functional Near-infrared Spectroscopy Hyperscanning Study.
How two brains communicate with each other during social interaction is highly dynamic and complex. Multi-person (i.e., hyperscanning) studies to date have focused on analyzing the entire time series of brain signals to reveal an overall pattern of inter-brain synchrony (IBS). However, this approach does not account for the dynamic nature of social interaction. In the present study, we propose a data-driven approach based on sliding windows and k-mean clustering to capture the dynamic modulation of IBS patterns during interactive cooperation tasks. We used a portable functional near-infrared spectroscopy (fNIRS) system to measure brain hemodynamic response between interacting partners (20 dyads) engaged in a creative design task and a 3D model building task. Results indicated that inter-personal communication during naturalistic cooperation generally presented with a series of dynamic IBS states along the tasks. Compared to the model building task, the creative design task appeared to involve more complex and active IBS between multiple regions in specific dynamic IBS states. In summary, the proposed approach stands as a promising tool to distill complex inter-brain dynamics associated with social interaction into a set of representative brain states with more fine-grained temporal resolution. This approach holds promise for advancing our current understanding of the dynamic nature of neurocognitive processes underlying social interaction.
View details for DOI 10.1016/j.neuroimage.2021.118263
View details for PubMedID 34126210
- Inter-Brain Synchrony and Innovation in a Zoom World Using Analog and Digital Manipulatives Design Thinking Research Springer. 2021
A Methodological Review of fNIRS in Driving Research: Relevance to the Future of Autonomous Vehicles.
Frontiers in human neuroscience
2021; 15: 637589
As automobile manufacturers have begun to design, engineer, and test autonomous driving systems of the future, brain imaging with functional near-infrared spectroscopy (fNIRS) can provide unique insights about cognitive processes associated with evolving levels of autonomy implemented in the automobile. Modern fNIRS devices provide a portable, relatively affordable, and robust form of functional neuroimaging that allows researchers to investigate brain function in real-world environments. The trend toward "naturalistic neuroscience" is evident in the growing number of studies that leverage the methodological flexibility of fNIRS, and in doing so, significantly expand the scope of cognitive function that is accessible to observation via functional brain imaging (i.e., from the simulator to on-road scenarios). While more than a decade's worth of study in this field of fNIRS driving research has led to many interesting findings, the number of studies applying fNIRS during autonomous modes of operation is limited. To support future research that directly addresses this lack in autonomous driving research with fNIRS, we argue that a cogent distillation of the methods used to date will help facilitate and streamline this research of tomorrow. To that end, here we provide a methodological review of the existing fNIRS driving research, with the overarching goal of highlighting the current diversity in methodological approaches. We argue that standardization of these approaches will facilitate greater overlap of methods by researchers from all disciplines, which will, in-turn, allow for meta-analysis of future results. We conclude by providing recommendations for advancing the use of such fNIRS technology in furthering understanding the adoption of safe autonomous vehicle technology.
View details for DOI 10.3389/fnhum.2021.637589
View details for PubMedID 33967721
- Dyadic Sex Composition and Task ClassificationUsing fNIRS Hyperscanning Data 2021
- Unobtrusive stress sensing via a commercial steering wheel. Scientific Reports 2021
Capturing Human Interaction in the Virtual Age: A Perspective on the Future of fNIRS Hyperscanning
FRONTIERS IN HUMAN NEUROSCIENCE
2020; 14: 588494
Advances in video conferencing capabilities combined with dramatic socio-dynamic shifts brought about by COVID-19, have redefined the ways in which humans interact in modern society. From business meetings to medical exams, or from classroom instruction to yoga class, virtual interfacing has permeated nearly every aspect of our daily lives. A seemingly endless stream of technological advances combined with our newfound reliance on virtual interfacing makes it likely that humans will continue to use this modern form of social interaction into the future. However, emergent evidence suggests that virtual interfacing may not be equivalent to face-to-face interactions. Ultimately, too little is currently understood about the mechanisms that underlie human interactions over the virtual divide, including how these mechanisms differ from traditional face-to-face interaction. Here, we propose functional near-infrared spectroscopy (fNIRS) hyperscanning-simultaneous measurement of two or more brains-as an optimal approach to quantify potential neurocognitive differences between virtual and in-person interactions. We argue that increased focus on this understudied domain will help elucidate the reasons why virtual conferencing doesn't always stack up to in-person meetings and will also serve to spur new technologies designed to improve the virtual interaction experience. On the basis of existing fNIRS hyperscanning literature, we highlight the current gaps in research regarding virtual interactions. Furthermore, we provide insight into current hurdles regarding fNIRS hyperscanning hardware and methodology that should be addressed in order to shed light on this newly critical element of everyday life.
View details for DOI 10.3389/fnhum.2020.588494
View details for Web of Science ID 000589689700001
View details for PubMedID 33240067
View details for PubMedCentralID PMC7669622
- Functional Near-Infrared Spectroscopy (fNIRS) in an Aerospace Environment: Challenges and Considerations AEROSPACE MEDICINE AND HUMAN PERFORMANCE 2020; 91 (10): 833–35
- Mayday, Mayday, Mayday: Using salivary cortisol to detect distress (and eustress!) in critical incident training INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS 2020; 78
Calm Commute: Guided Slow Breathing for Daily Stress Management in Drivers
Interactive, Mobile, Wireless, Ubiquitous Technologies
View details for DOI 10.1145/3380998
Back to School: Impact of Training on Driver Behavior and State in Autonomous Vehicles
IEEE. 2020: 1189-1196
View details for Web of Science ID 000653124200178
- The Neuroscience of Team Cooperation versus Team Collaboration Design Thinking Research Springer. 2020
- On-road Guided Slow Breathing Interventions for Car Commuters ASSOC COMPUTING MACHINERY. 2019
- On-road Stress Analysis for In-car Interventions During The Commute ASSOC COMPUTING MACHINERY. 2019
- Breath Booster! Exploring In-Car, Fast-Paced Breathing Interventions to Enhance Driver Arousal State ASSOC COMPUTING MACHINERY. 2018: 128-137
- Just Breathe: In-Car Interventions for Guided Slow Breathing Journal of ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018
- Driving with the Fishes: Towards Calming and Mindful Virtual Reality Experiences for the Car Journal of ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018
- Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices JOURNAL OF INTELLIGENT MANUFACTURING 2017; 28 (7): 1585-1607
- Learning-by-Doing: Using Near Infrared Spectroscopy to Detect Habituation and Adaptation in Automated Driving ASSOC COMPUTING MACHINERY. 2017: 134-143
- Assessing Driver Cortical Activity during Varying Levels of Automation with Functional Near Infrared Spectroscopy 2017