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)