Rose Faghih

In the ever-evolving landscape of workplace dynamics, the intricate dance between stress and productivity takes center stage. A recent study, spanning various disciplines and delving into the depths of neuroscience, sheds light on this complex relationship, challenging conventional wisdom and opening new pathways for understanding how to improve productivity.

At the heart of this exploration lies the Yerkes-Dodson law, a theory proposing an optimal level of stress conducive to peak productivity. Yet, as researchers unveil, the universality of this law remains under scrutiny, prompting deeper dives into the nuances of stress-response across different contexts and populations.

Drawing from neuroscience, researchers from NYU Tandon led by Rose Faghih, Associate Professor of Biomedical Engineering, have published a study illuminating the role of autonomic nervous system, which is directly influenced by key brain regions — like the amygdala, prefrontal cortex, and hippocampus — in shaping our responses to stress and influencing cognitive functions. These insights not only deepen our comprehension of stress but also offer pathways to enhance cognitive performance.

The researchers’ approach is innovative, in that it concurrently tracks cognitive arousal and expressive typing states, employing sophisticated multi-state Bayesian filtering techniques. This allows them to paint a picture of how physiological responses and cognitive states interplay to influence productivity. 

One particularly innovative aspect of the study involves typing dynamics as a measure of cognitive engagement and emotional expression. By examining typing patterns and  brain autonomic nervous system activation, researchers gain insights into individuals' cognitive states, especially relevant in remote work environments. The integration of typing dynamics into the analysis provides a tangible link between internal cognitive processes and externalized behaviors.

“With the rise of remote work, understanding how stress impacts productivity takes on newfound significance,” Faghih says. “By uncovering the mechanisms at play, we’re paving the way for developing tools and strategies to eventually optimize performance and well-being in remote settings.”

Moreover, the study's methodology, grounded in sophisticated Bayesian models, promises not only to validate existing theories but also to unveil new patterns and insights. As the discussion turns to practical applications, the potential for integrating these findings into ergonomic workspaces and mental health support systems becomes apparent.

This research offers a glimpse into the intricate web of stress, productivity, and cognition. As we navigate the evolving landscape of work, understanding these dynamics becomes paramount, paving the way for a more productive, resilient workforce.

Alam, S., Khazaei, S., & Faghih, R. T. (2024). Unveiling productivity: The interplay of cognitive arousal and expressive typing in remote work. PLOS ONE, 19(5). https://doi.org/10.1371/journal.pone.0300786

Human brain states are unobserved states that can constantly change due to internal and external factors, including cognitive arousal, a.k.a. intensity of emotion, and cognitive performance states. Maintaining a proper level of cognitive arousal may result in being more productive throughout daily cognitive activities. Therefore, monitoring and regulating one’s arousal state based on cognitive performance via simple everyday interventions such as music is a critical topic to be investigated. 

Researchers from NYU Tandon led by Rose Faghih — inspired by the Yerkes-Dodson law in psychology, known as the inverted-U law — investigated the arousal-performance link throughout a cognitive task in the presence of personalized music. The Yerkes-Dodson law states that performance is a function of arousal and has an inverted-U shaped relationship with cognitive arousal, i.e., a moderate level of arousal results in optimal performance, on the other hand, an excessively high level of arousal may result in anxiety, while a deficient level of arousal may be followed by boredom. 

In this study, participants selected music with calming and exciting music components to mimic the low and high-arousing environment. To decode the underlying arousal and performance with respect to everyday life settings, they used peripheral physiological data as well as behavioral signals within the Bayesian Decoders. In particular, electrodermal activity (EDA) has been widely used as a quantitative arousal index. In parallel, behavioral data such as a sequence of correct/incorrect responses and reaction time are common cognitive performance observations. 

The decoded arousal and performance data points in the arousal-performance frame depict an inverted U shape, which conforms with the Yerkes-Dodson law. Also, findings present the overall better performance of participants within the exciting background music. Considering the Yerkes-Dodson law, we develop a performance-based arousal decoder that can preserve and account for the cognitive performance dynamic. Such a decoder can provide a profound insight into how physiological responses and cognitive states interplay to influence productivity.

Although several factors, such as the nature of the cognitive task, the participant’s baseline, and the type of applied music, can impact the outcome, it might be feasible to enhance cognitive performance and shift one’s arousal from either the left or right side of the curve using music. In particular, the baseline of arousal level varies among humans, and the music may be selected to set the arousal within the desired range. The outcome of this research can advance researchers closer to developing a practical and personalized closed-loop brain-computer interface for regulating internal brain states within everyday life activities.

S. Khazaei, M. R. Amin, M. Tahir and R. T. Faghih, "Bayesian Inference of Hidden Cognitive Performance and Arousal States in Presence of Music," in IEEE Open Journal of Engineering in Medicine and Biology, doi: 10.1109/OJEMB.2024.3377923.

The human body can be viewed as a complex collection of interrelated control systems. All these systems quietly function to maintain different variables or states within our bodies. Now the states themselves aren’t always easily accessible, but the physiological changes and signals they give rise to, in a number of cases, are. Hence, one can view the human body through the lens of control theory, and this allows the mathematical tools typically used in control engineering to be applied to understanding physiology. 

This is indeed the case with certain states such as emotion, cognition and energy — the values themselves aren’t easily accessible, but the changes in sweat secretions, heart rate and hormone secretions can indeed be measured. Consequently, we can use tools from control systems to estimate these unknown quantities. 

In several cases, the observations to which the underlying state variables are related to are “spikey” or pulsatile in appearance and nature. Skin conductance is one such example. Bursts of neural activity to the sweat glands are responsible for the spiky appearance of a skin conductance signal. Likewise, the hormone cortisol — the body’s main stress hormone — is also secreted in pulses. Moreover, both cortisol and skin conductance signals can be modeled in strikingly similar ways mathematically. In the case of skin conductance and cortisol however, the signals aren’t purely spikey or pulsatile in nature, but are also accompanied by or incorporate a continuous-valued baseline or biochemical concentration level. 

Previous work attempting to estimate underlying aspects of emotion from skin conductance or energy levels from cortisol have not considered this specific formulation of the observations. In our present work, we consider modeling the observations from skin conductance and cortisol as a marked point process (MPP) coupled together with a continuous-valued variable. This matches the inherent constituent components of the signals much better.

Researchers at NYU Tandon led by Rose Faghih, Associate Professor of Biomedical Engineering, developed a decoder to estimate an aspect of emotion known as sympathetic arousal from skin conductance and an energy state from cortisol measurements. The model was able to capture more subtle, fine-grained variations in the underlying states compared to estimates from some of our earlier models. Cortisol has a characteristic daytime vs. nighttime secretory pattern and their model was able to estimate energy levels consistent with these expectations. Estimates of sympathetic arousal based on skin conductance were also high during certain stressors and lower during relaxation. 

They also obtained sympathetic arousal estimates that were consistent with blood flow patterns in the brain in an additional experiment as well. Here, blood flow was measured using a technique known as functional Near Infrared Spectroscopy (fNIRS). The algorithm l they developed on this occasion also had a superior capability with dealing with what is known as overfitting in comparison to our earlier methods. 

D. S. Wickramasuriya, S. Khazaei, R. Kiani and R. T. Faghih, "A Bayesian Filtering Approach for Tracking Sympathetic Arousal and Cortisol-Related Energy From Marked Point Process and Continuous-Valued Observations," in IEEE Access, vol. 11, pp. 137204-137247, 2023, doi: 10.1109/ACCESS.2023.3334974

UPDATE March 4, 2024: The data set that Faghih’s lab collected for this research is now available to the global research community on the PhysioNet platform. This dataset is unique, offering real-world insights into how common pleasures affect our physiological responses and cognitive performance.

The potential of this dataset is vast. It opens new avenues for research into the influence of everyday experiences on cognitive performance, potentially leading to smarter work environments or personalized life-enhancing strategies. Imagine tailoring your work environment with specific sounds or scents to boost productivity and creativity. By analyzing this dataset, researchers can discover patterns and connections previously unseen. This could lead to breakthroughs in understanding how to harness everyday experiences to enhance cognitive abilities. Ultimately, this research could pave the way for innovative applications in workplace productivity enhancement and educational method improvement.

“This dataset is more than a collection of data points; it is a window into the intricate relationship between daily pleasures and our brain's performance,” says Fekri Azgomi, Faghih’s former PhD student who collected this data. “As our lab, the Computational Medicine Laboratory, shares this dataset with the world, we are excited about the endless possibilities it holds for advancing our understanding of the human mind and enhancing everyday life.”

Original story below.

Listening to music and drinking coffee are the sorts of everyday pleasures that can impact a person’s brain activity in ways that improve cognitive performance, including in tasks requiring concentration and memory.

That’s a finding of a new NYU Tandon School of Engineering study involving MINDWATCH, a groundbreaking brain-monitoring technology.

Developed over the past six years by NYU Tandon's Biomedical Engineering Associate Professor Rose Faghih, MINDWATCH is an algorithm that analyzes a person's brain activity from data collected via any wearable device that can monitor electrodermal activity (EDA). This activity reflects changes in electrical conductance triggered by emotional stress, linked to sweat responses.

In this recent MINDWATCH study, published in Nature Scientific Reports, subjects wearing  skin-monitoring wristbands and brain monitoring headbands completed cognitive tests while listening to music, drinking coffee and sniffing perfumes reflecting their individual preferences. They also completed those tests without any of those stimulants. 

The MINDWATCH algorithm revealed that music and coffee measurably altered subjects’ brain arousal, essentially putting them in a physiological “state of mind” that could modulate their performance in the working memory tasks they were performing. 

Specifically, MINDWATCH determined the stimulants triggered increased “beta band” brain wave activity, a state associated with peak cognitive performance. Perfume had a modest positive effect as well, suggesting the need for further study. 

“The pandemic has impacted the mental well-being of many people across the globe and now more than ever, there is a need to seamlessly monitor the negative impact of everyday stressors on one's cognitive function,” said Faghih. “Right now MINDWATCH is still under development, but our eventual goal is that it will contribute to technology that could allow any person to monitor his or her own brain cognitive arousal in real time, detecting moments of acute stress or cognitive disengagement, for example. At those times, MINDWATCH could ‘nudge’ a person towards simple and safe interventions — perhaps listening to music  — so they could get themselves into a brain state in which they feel better and perform job or school tasks more successfully.”

The specific cognitive test used in this study — a working memory task, called the n-back test — involves presenting a sequence of stimuli (in this case, images or sounds) one by one and asking the subject to indicate whether the current stimulus matches the one presented "n" items back in the sequence. This study employed a 1-back test — the participant responded "yes" when the current stimulus is the same as the one presented one item back — and a more challenging 3-back test, asking the same for three items back.

Researchers tested three types of music - energetic and relaxing music familiar to the subject, as well as novel AI-generated music that reflected the subject’s tastes.  Consistent with prior MINDWATCH research, familiar energetic music delivered bigger performance gains — as measured by reaction times and correct answers — than relaxing music. While AI-generated music produced the biggest gains among all three, further research is needed to confirm those results.

Drinking coffee led to notable but less-pronounced performance gains than music, and perfume had the most modest gains.

Performance gains under all stimulations tended to be higher on the 3-back tests, suggesting interventions may have the most profound effect when “cognitive load” is higher.

Ongoing experimentation by the MINDWATCH team will confirm the efficacy of the technology’s ability to monitor brain activity consistently, and the general success of various interventions in modulating that brain activity. Determining a category of generally successful interventions does not mean that any individual person will find it works for them.

The research was performed as a part of Faghih’s National Science Foundation CAREER award on the Multimodal Intelligent Noninvasive brain state Decoder for Wearable AdapTive Closed-loop arcHitectures (MINDWATCH) project.  The study's diverse dataset is available to researchers, allowing additional research on the use of the safe interventions in this study to modulate brain cognitive states.

Faghih served as the senior author for this paper. Its first author is Hamid Fekri Azgomi, who earned his Ph.D. under Faghih and is now a postdoctoral scholar of neurological surgery at the University of California San Francisco School of Medicine.

Fekri Azgomi, H., F. Branco, L.R., Amin, M.R. et al. Regulation of brain cognitive states through auditory, gustatory, and olfactory stimulation with wearable monitoring. Sci Rep 13, 12399 (2023). https://doi.org/10.1038/s41598-023-37829-z