@inproceedings{823b0c5ebdef4a96841162f77a62b3cd,
title = "Entropic brain-computer interfaces using fNIRS & EEG to measure attentional states in a Bayesian framework",
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.",
keywords = "ADHD, Attention, BCI, Bidirectional brain-computer interface, Brain-computer interface, Default mode network, EEG, Entropic brain-computer interface, Entropy, FNIRS, Implicit interface, Meditation, Physiological computing, Task-positive network, Workload",
author = "Hincks, {Samuel W.} and Sarah Bratt and Sujit Poudel and Phoha, {Vir V.} and Jacob, {Robert J.K.} and Dennett, {Daniel C.} and Hirshfield, {Leanne M.}",
note = "Funding Information: We thank Beste Yuksel, Daniel Afergan, Evan Peck, Erin Solovey, Leanne Hirshfield, Tomoki Shibata, Anzu Hakone, Ronna ten Brink, Min Bu, Sonal Chatter, Yan Huo, Eun Youb Lee, Bushra Alkadhi, Feiyu Lu, Mary Skitka, Nick Sempere, Maya DeBellis, Tal August, James Carney, Nik Liolios, Beibei Du, and Calvin Liang who are students and alumni of the HCI group at Tufts. We thank Remco Chang, Erika Hussey, Tad Brunye, Sergio Fantini and Angelo Sas-saroli from Tufts University. We thank Stuart Hirsh-field from Hamilton College. We thank Google Inc. for support of this research. Vir V. Phoha was supported in part by National Science Foundation Award SaTC Number: 1527795. Publisher Copyright: {\textcopyright} 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 4th International Conference on Physiological Computing Systems, PhyCS 2017 ; Conference date: 27-07-2017 Through 28-07-2017",
year = "2017",
language = "English (US)",
series = "PhyCS 2017 - Proceedings of the 4th International Conference on Physiological Computing Systems",
publisher = "SciTePress",
pages = "23--34",
editor = "Andreas Holzinger and {da Silva}, {Hugo Placido} and Alan Pope",
booktitle = "PhyCS 2017 - Proceedings of the 4th International Conference on Physiological Computing Systems",
}