A Physical Paradigm for Bidirectional Brain-Computer Interfaces.
Hincks, Samuel.
2019
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This dissertation
deepens research into interfaces that supplement input the user transmits to the
computer intentionally with an auxiliary channel describing ongoing brain activation.
Existing implementations of such implicit brain-computer interfaces (BCI) depend on
machine learning algorithms trained to distinguish physiological signals detected with
functional near-infrared spectroscopy ... read more(fNIRS) under different task conditions, which
subsequent chapters will refer to as neuracles. When calibrated to the user's brain, the
implicit BCI adjusts settings in the interface to better match the mental state that has
unfolded. Because this approach does not depend on an understanding about the
relationship between fNIRS signals and physical activity in the brain, I will refer to
the current methodology for studying and building implicit BCIs as the agnostic
paradigm. Experiments evaluating implicit BCIs in the agnostic paradigm have led to
measurable improvements in user performance in a number of controlled laboratory
experiments. This dissertation introduces a descendant of implicit BCI, referred to as a
bidirectional BCI. Instead of adapting the interface to match the mental state that has
unfolded, a bidirectional BCI strives to adapt outputs to the brain to stimulate and
maintain optimal mental states for its user. This new class of BCI depends on
discovering a model for the interaction between brain and computer at four levels of
analysis. Such a model should account for how the brain works at the physical level, the
linkup between brain state and mental state at a mental level, the relationship between
brain state and sensor data at the neuracle level, as well as how computer settings and
output affect the physical state of the brain at an interface level. With a synchronized
model at these four levels, a bidirectional BCI can establish a feedback loop between
the user's brain and its methods to affect the brain's state, and deploy machine
learning algorithms to adjust output to the brain to coerce and sustain desirable mental
states. But bidirectional BCIs are not possible with the existing agnostic paradigm.
This dissertation therefore develops an alternative method, which has a synchronized
understanding of brain-computer interaction at physical, mental, neuracle, and interface
levels. Scientific progress towards physical neuracles depends on methods for studying
one's own brain as a scientist and engineer as well as the brains of other lucid
individuals synchronized on a common vocabulary for describing mental states. This
alternative methodology is facilitated by the Neuracle software distributed as part of
this dissertation, which consists of interfaces, visualizations, and signal processing
algorithms needed in a physical paradigm for BCI. This dissertation makes several
contributions to the study of Brain-Computer Interfaces. Together, the following
chapters 1. Extend existing unidirectional models for BCI to a bidirectional modality.
2. Define a physical paradigm for BCIs, which does not depend on machine learn- ing to
measure the user's cognitive state. 3. Distribute software that enables an
introspectively oriented methodology needed in the physical paradigm. 4. Identify
methods for influencing the state of the brain by altering the predictability of
information coming from the computer.
Thesis (Ph.D.)--Tufts University, 2019.
Submitted to the Dept. of Computer Science.
Advisor: Robert Jacob.
Committee: Remco Chang, Leanne Hirshfield, Daniel Dennett, and Mark Sheldon.
Keywords: Computer science, Cognitive psychology, and Music theory.read less - ID:
- 9g54xx04p
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