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Authors: Samuel Hincks 1 ; Sarah Bratt 2 ; Sujit Poudel 2 ; Vir V. Phoha 2 ; Robert J. K. Jacob 1 ; Daniel C. Dennett 1 and Leanne Hirshfield 2

Affiliations: 1 Tufts University, United States ; 2 Syracuse University, United States

Keyword(s): BCI, Brain-computer Interface, fNIRS, EEG, Workload, Implicit Interface, Attention, Task-positive Network, Default Mode Network, Entropy, Physiological Computing, Entropic Brain-computer Interface, Bidirectional Brain-computer Interface, ADHD, Meditation.

Abstract: Implicit Brain-Computer Interfaces (BCI) adapt system settings subtly based on real time measures of brain activation without the user’s explicit awareness. For example, measures of the user’s cognitive profile might drive a system that alters the timing of notifications in order to minimize user interruption. Here, we consider new avenues for implicit BCI based on recent discoveries in cognitive neuroscience and conduct a series of experiments using BCI’s principal non-invasive brain sensors, fNIRS and EEG. We show how Bayesian and systems neuroscience formulations explain the difference in performance of machine learning algorithms trained on brain data in different conditions. These new formulations posit that the brain aims to minimize its long-term surprisal of sensory data and organizes its calculations on two anti-correlated networks. We consider how to use real-time input that portrays a user along these dimensions in designing Bidirectional BCIs, which are Implicit BCIs that aim to optimize the user’s state by modulating computer output based on feedback from a brain monitor. We introduce Entropic Brain-Computer Interfacing as a type of Bidirectional BCI which uses physiological measurements of information theoretical dimensions of the user’s state to evaluate the digital flow of information to the user’s brain, tweaking this output in a feedback loop to the user’s benefit. (More)

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Paper citation in several formats:
Hincks, S.; Bratt, S.; Poudel, S.; Phoha, V.; K. Jacob, R.; Dennett, D. and Hirshfield, L. (2017). Entropic Brain-computer Interfaces. In Proceedings of the 4th International Conference on Physiological Computing Systems - PhyCS; ISBN 978-989-758-268-4; ISSN 2184-321X, SciTePress, pages 23-34. DOI: 10.5220/0006383300230034

@conference{phycs17,
author={Samuel Hincks. and Sarah Bratt. and Sujit Poudel. and Vir V. Phoha. and Robert J. {K. Jacob}. and Daniel C. Dennett. and Leanne Hirshfield.},
title={Entropic Brain-computer Interfaces},
booktitle={Proceedings of the 4th International Conference on Physiological Computing Systems - PhyCS},
year={2017},
pages={23-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006383300230034},
isbn={978-989-758-268-4},
issn={2184-321X},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Physiological Computing Systems - PhyCS
TI - Entropic Brain-computer Interfaces
SN - 978-989-758-268-4
IS - 2184-321X
AU - Hincks, S.
AU - Bratt, S.
AU - Poudel, S.
AU - Phoha, V.
AU - K. Jacob, R.
AU - Dennett, D.
AU - Hirshfield, L.
PY - 2017
SP - 23
EP - 34
DO - 10.5220/0006383300230034
PB - SciTePress