The Department of Biomedical Engineering’s newest faculty member is pioneering new, brain-aware wearables
NYU Biomedical Engineering welcomed Rose with a news article feature. The feature reads: “MINDWATCH. While it may sound like an exciting hit show on Netflix, it’s actually an acronym for a project from Associate Professor Rose Faghih, a new member of the Department of Biomedical Engineering. It stands for Multimodal Intelligent Noninvasive brain state Decoder for Wearable AdapTive Closed-loop arcHitectures, which she has …”
CML graduate students presented their research at the 2021 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Conference (EMBC) and published three conference papers in proceedings of EMBC. Their research mainly described the impact of different types of music on cognitive arousal and performance during working memory experiments performed at CML. The conference papers focused on decoding cognitive arousal using skin conductance variations and functional near-infrared spectroscopy (fNIRS) data, characterizing cognitive performance based on behavioral data, and quantifying the performance as a function of cognitive arousal with the goal of eventually designing controllers that use music as an actuator to regulate arousal and improve performance. The experimental dataset used in these publications was collected by CML before the pandemic; those interested in collaborations on this dataset can contact Rose at rfaghih@nyu.edu to access the data. CML plans to make the dataset publicly available in the next couple of years for the public to use.
Md. Rafiul Amin defended his thesis “Scalable and Robust Inference of Sparse Brain Signals via Personalized Physiological System Identification”. He has joined Aeva Inc as a DSP Characterization Engineer.
Luciano Branco defended his thesis “Closed-Loop Control of Brain States using Physiological Signals from Wearable Devices”. Luciano has started his PhD in Biomedical Engineering at the University of Houston.
Saman Khazaei defended his thesis “Bayesian Decoder Design for Investigation of Cognitive Arousal and Performance Using Physiological and Behavioral Data”. Saman has started his PhD in Biomedical Engineering at NYU’s CML.
Acknowledgment: This material is based upon work supported by the National Science Foundation under Grant Number 1755780 Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Smartwatch-like wearables have enabled seamless tracking of vital signs and physical activities, but still lack a significant feature: they are currently unable to provide any information about brain states or to modulate brain function for optimizing human health and performance. This project aims to make it possible for wearables to feature such capabilities. Being aware of brain states is not only extremely valuable in clinical studies but is also crucial to improving human performance in various everyday life activities. While recording neural signals directly from the scalp region is possible, it is impractical for use in everyday life. In order to fill this gap, the goal of this project is to pioneer a closed-loop brain-aware wearable architecture called MINDWATCH. This enables (1) decoding multidimensional brain states from noninvasive wearable devices and (2) applying corrective control. MINDWATCH will transform healthcare delivery (e.g., aging, autism, dementia) as well as human performance and productivity enhancement (e.g., online learning, smart workplaces). For instance, knowledge of mental health and cognitive engagement can enable detecting if a student is depressed or is not cognitively engaged/learning, which makes it possible to take corrective action early on.
This project seeks to overcome the barriers to achieving brain-aware wearables by pioneering a transformative system-theoretic computational toolset for noninvasive closed-loop wearable architectures that monitor and modulate brain function without needing neural recordings. The proposed framework will (1) infer discrete brain-related events in real-world settings, (2) decode multidimensional latent neurobehavioral states based on inferred brain activity, and (3) apply robust adaptive control to maintain the neurobehavioral states within desired ranges. The closed-loop framework will be rigorously validated using experiments on interactive human-technology environments and mental health.
[2] Amin, M.R, Tahir, M., and Faghih, R.T.,”An Investigation of music impacts on cognitive Performance and Arousal in the presence of Yerkes-Dodson Law.”The Annual International Conference of the IEEE Engineering in Medicine and Biology Society,2021.
[3] Yaghmour, A., Amin, M.R, and Faghih, R.T., “Decoding a Music-Modulated Cognitive Arousal State using Electrodermal Activity and Functional Near-infrared Spectroscopy Measurements.”The Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2021.
[4] Khazaei, S., Amin, M.R, and Faghih, R.T., “Decoding a Neurofeedback-Modulated Cognitive Arousal State to Investigate Performance Regulation by the Yerkes-Dodson Law.”The Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2021
R. T. Faghih., Wickramasuriya, D.S., . SYSTEMS AND METHODS FOR ESTIMATING A NERVOUS SYSTEM STATE BASED ON MEASUREMENT OF A PHYSIOLOGICAL CONDITIONU.S. Provisional Patent Application No.: 63/110,48
MIT Technology Review selected Rose as one of the visionaries in its list of Innovators Under 35for the year 2020. The recognition highlighted her idea of sensor-laden wrist-watches that could monitor states of the brain traditionally accessed via neuroimaging technologies such as EEG. The idea would not only have clinical relevance for treating patients with neuropsychiatric or hormone disorders but would also have applicability to people who want to improve everyday health as well. Futuroprossimo, an Italian futurology magazine, also highlighted her as one of the three MIT Technology Review’s 2020 Innovators Under 35 female awardees, who work on an innovation that has the highest potential for a future Nobel prize.