The Science Behind Enten By Neurable

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Dr. Mavi Ruiz-Blondet

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Do you want to learn more about how Neurable’s technology works? Check out our white paper.

Enten translates brain activity into simple and actionable insights about your focus level throughout the day. The team at Neurable has been working diligently to verify that Enten collects meaningful brain signals and that our models of attention and focus are accurate and mainly driven by brain activity.

Figure references are in the white paper. In our white paper we validate that we:

Collected good quality brain data from around the ear compared to a gold-standard medical grade system in three well known experimental paradigms:
- Increased alpha oscillations when closing the eyes (Fig. 2 and 3)
- Auditory Steady-State Response in response to a modulated tone (Fig 4)
- P300 in response to target stimuli (Fig. 5)
Saw a connection between behavior and attention (Fig. 10 - 12)
Created a model that estimates attention from neural sources with an accuracy of 80% ± 4%) (Table 1)
Maintained performance, validating our technology over time, across individuals, and in less controlled environments. (Tested across a diverse group of 132 people) (Fig. 17)

We ran 45 novel experiments, collected over 20,000 minutes of brain data, all to ensure that Enten is the most scientifically robust and performative consumer BCI product

Publications

The team at Neurable is backed by a wealth of experience with neural activity and building models. Below are selected publications from members of the Neurable team:

Valeriani, Davide, Riccardo Poli, and Caterina Cinel. "Enhancement of group perception via a collaborative brain–computer interface." IEEE Transactions on Biomedical Engineering 64, no. 6 (2016): 1238-1248. http://dx.doi.org/10.1109/TBME.2016.2598875

Valeriani, Davide, Caterina Cinel, and Riccardo Poli. "Group augmentation in realistic visual-search decisions via a hybrid brain-computer interface." Scientific reports 7, no. 1 (2017): 1-12.http://dx.doi.org/10.1038/s41598-017-08265-7

Stanley, David A., Jefferson E. Roy, Mikio C. Aoi, Nancy J. Kopell, and Earl K. Miller. "Low-beta oscillations turn up the gain during category judgments." Cerebral Cortex 28, no. 1 (2018): 116-130.https://academic.oup.com/cercor/article/28/1/116/2573020login=true

Stanley, David A., Arnaud Y. Falchier, Benjamin R. Pittman-Polletta, Peter Lakatos, Miles A. Whittington, Charles E. Schroeder, and Nancy J. Kopell. "Flexible reset and entrainment of delta oscillations in primate primary auditory cortex: modeling and experiment." bioRxiv (2019): 812024. https://www.biorxiv.org/content/biorxiv/early/2019/10/22/812024.full.pdf

Blondet, Maria V. Ruiz, Adarsha Badarinath, Chetan Khanna, and Zhanpeng Jin. "A wearable real-time BCI system based on mobile cloud computing." In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 739-742. IEEE, 2013. https://ieeexplore.ieee.org/abstract/document/6696040/

Ruiz-Blondet, Maria V., Zhanpeng Jin, and Sarah Laszlo. "CEREBRE: A novel method for very high accuracy event-related potential biometric identification." IEEE Transactions on Information Forensics and Security 11, no. 7 (2016): 1618-1629. https://ieeexplore.ieee.org/abstract/document/7435286/

Yousefi, Ali, Angelique C. Paulk, Ishita Basu, Jonathan L. Mirsky, Darin D. Dougherty, Emad N. Eskandar, Uri T. Eden, and Alik S. Widge. "COMPASS: an open-source, general-purpose software toolkit for computational psychiatry." Frontiers in neuroscience 12 (2019): 957. https://www.frontiersin.org/articles/10.3389/fnins.2018.00957/full

Yousefi, Ali, Ishita Basu, Angelique C. Paulk, Noam Peled, Emad N. Eskandar, Darin D. Dougherty, Sydney S. Cash, Alik S. Widge, and Uri T. Eden. "Decoding hidden cognitive states from behavior and physiology using a bayesian approach." Neural computation 31, no. 9 (2019): 1751-1788. https://direct.mit.edu/neco/article/31/9/1751/8498/Decoding-Hidden-Cognitive-States-From-Behavior-and

Video explanation:

Ruiz-Blondet, Maria V., Zhanpeng Jin, and Sarah Laszlo. "Permanence of the CEREBRE brain biometric protocol." Pattern Recognition Letters 95 (2017): 37-43. https://www.sciencedirect.com/science/article/pii/S0167865517301940

Armstrong, Blair C., Maria V. Ruiz-Blondet,Negin Khalifian, Kenneth J. Kurtz, Zhanpeng Jin, and Sarah Laszlo. "Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics." Neurocomputing 166 (2015): 59-67.Armstrong, Blair C., Maria V. Ruiz-Blondet, Negin Khalifian, Kenneth J. Kurtz, Zhanpeng Jin, and Sarah Laszlo. "Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics." Neurocomputing 166 (2015): 59-67. https://www.sciencedirect.com/science/article/pii/S0925231215004725

Laszlo, Sarah, Maria Ruiz-Blondet, Negin Khalifian, Fanny Chu, and Zhanpeng Jin. "A direct comparison of active and passive amplification electrodes in the same amplifier system." Journal of neuroscience methods 235 (2014): 298-307. https://www.sciencedirect.com/science/article/pii/S0165027014001666

Gui, Qiong, Maria V. Ruiz-Blondet, Sarah Laszlo, and Zhanpeng Jin. "A survey on brain biometrics." ACM Computing Surveys (CSUR) 51, no. 6 (2019): 1-38. https://dl.acm.org/doi/abs/10.1145/3230632

Valeriani, Davide, and Kristina Simonyan. "A microstructural neural network biomarker for dystonia diagnosis identified by a DystoniaNet deep learning platform." Proceedings of the National Academy of Sciences 117, no. 42 (2020): 26398-26405. https://doi.org/10.1073/pnas.2009165117

Fernandez-Vargas, Jacobo, Christoph Tremmel, Davide Valeriani, Saugat Bhattacharyya, Caterina Cinel, Luca Citi, and Riccardo Poli. "Subject-and task-independent neural correlates and prediction of decision confidence in perceptual decision making." Journal of Neural Engineering 18, no. 4 (2021): 046055. https://doi.org/10.1088/1741-2552/abf2e4

Cinel, Caterina, Davide Valeriani, and Riccardo Poli. "Neurotechnologies for human cognitive augmentation: current state of the art and future prospects." Frontiers in human neuroscience 13 (2019): 13. https://doi.org/10.3389/fnhum.2019.00013

Poli, Riccardo, Davide Valeriani, and CaterinaCinel. "Collaborative brain-computer interface for aiding decision-making." PloS one 9, no. 7 (2014): e102693. http://dx.doi.org/10.1371/journal.pone.0102693

Valeriani, Davide, Riccardo Poli, and Caterina Cinel. "A collaborative Brain-Computer Interface for improving group detection of visual targets in complex natural environments." In 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 25-28. IEEE, 2015. http://dx.doi.org/10.1109/NER.2015.7146551

Valeriani, Davide, Riccardo Poli, and Caterina Cinel. "A collaborative Brain-Computer Interface to improve human performance in a visual search task." In 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 218-223. IEEE, 2015. http://dx.doi.org/10.1109/NER.2015.7146599

Valeriani, Davide, and Ana Matran-Fernandez. "Towards a wearable device for controlling a smartphone with eye winks." In 2015 7th Computer Science and Electronic Engineering Conference (CEEC), pp. 41-46. IEEE, 2015. http://dx.doi.org/10.1109/CEEC.2015.7332697

Valeriani, Davide, Caterina Cinel, and Riccardo Poli. "Augmenting group performance in target-face recognition via collaborative brain-computer interfaces for surveillance applications." In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 415-418. IEEE, 2017. http://dx.doi.org/10.1109/NER.2017.8008378

Bhattacharyya, Saugat, Davide Valeriani, Caterina Cinel, Luca Citi, and Riccardo Poli. "Target Detection in Video Feeds with Selected Dyads and Groups Assisted by Collaborative Brain-Computer Interfaces." In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 159-162. IEEE, 2019. https://doi.org/10.1109/NER.2019.8717146

Bhattacharyya, Saugat, Davide Valeriani, Caterina Cinel, Luca Citi, and Riccardo Poli. "Collaborative brain-computer interfaces to enhance group decisions in an outpost surveillance task." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3099-3102. IEEE, 2019. https://doi.org/10.1109/EMBC.2019.8856309

Fernandez-Vargas, Jacobo, Davide Valeriani, Caterina Cinel, Nitin Sadras, Parima Ahmadipour, Maryam M. Shanechi, Luca Citi, and Riccardo Poli. "Confidence Prediction from EEG Recordings in a Multisensory Environment." In Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology, pp. 269-275. 2020. https://doi.org/10.1145/3397391.3397426

Matran-Fernandez, A., D. Valeriani, and R. Poli. "Toward bcis out of the lab: Impact of motion artifacts on brain-computer interface performance." (2016): 219-239. https://doi.org/10.1201/b19682-9

Valeriani, Davide, and Ana Matran-Fernandez. "Past and future of multi-mind brain-computer interfaces." (2018): 685-700. https://doi.org/10.1201/9781351231954-36

Valeriani, Davide, Caterina Cinel, and Riccardo Poli. "Hybrid collaborative brain–computer interfaces to augment group decision-making." In Neuroergonomics, pp. 187-190. Academic Press, 2019. https://doi.org/10.1016/B978-0-12-811926-6.00031-2

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‍²Distraction Stroop Tasks experiment: The Stroop Effect (also known as cognitive interference) is a psychological phenomenon describing the difficulty people have naming a color when it's used to spell the name of a different color. During each trial of this experiment, we flashed the words “Red” or “Yellow” on a screen. Participants were asked to respond to the color of the words and ignore their meaning by pressing four keys on the keyboard –– “D”, “F”, “J”, and “K,” -- which were mapped to “Red,” “Green,” “Blue,” and “Yellow” colors, respectively. Trials in the Stroop task were categorized into congruent, when the text content matched the text color (e.g. Red), and incongruent, when the text content did not match the text color (e.g., Red). The incongruent case was counter-intuitive and more difficult. We expected to see lower accuracy, higher response times, and a drop in Alpha band power in incongruent trials. To mimic the chaotic distraction environment of in-person office life, we added an additional layer of complexity by floating the words on different visual backgrounds (a calm river, a roller coaster, a calm beach, and a busy marketplace). Both the behavioral and neural data we collected showed consistently different results in incongruent tasks, such as longer reaction times and lower Alpha waves, particularly when the words appeared on top of the marketplace background, the most distracting scene.

‍Interruption by Notification: It’s widely known that push notifications decrease focus level. In our three Interruption by Notification experiments, participants performed the Stroop Tasks, above, with and without push notifications, which consisted of a sound played at random time followed by a prompt to complete an activity. Our behavioral analysis and focus metrics showed that, on average, participants presented slower reaction times and were less accurate during blocks of time with distractions compared to those without them.

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