Real-time fNIRS Brain Input for Enhancing Interactive Systems.
human-computer interaction (HCI) techniques cannot fully capture the richness of the
user's thoughts and intentions when interacting with a computer system. For example,
when we communicate with other people, we do not simply use words, but also accompanying
cues that give the other person additional insight to our thoughts. At the same time,
several physiological changes occur... read morethat may or may not be detected by the other person.
When we communicate with computers, we also generate these additional signals, but the
computer cannot sense such signals, and therefore ignores them. Detecting these signals
in real time and incorporating them into the user interface could improve the
communication channel between the computer and the human user with little additional
effort required of the user. This communication improvement would lead to technology
that is more supportive of the user's changing cognitive state. Such improvements in
bandwidth are increasingly valuable, as technology becomes more powerful and pervasive,
while our cognitive abilities do not change considerably. In this dissertation, I
explore using brain sensor data as a passive, implicit input channel that expands the
bandwidth between the human and computer by providing supplemental information about the
user. Using a relatively new brain imaging tool called functional near-infrared
spectroscopy (fNIRS), we can detect signals within the brain that indicate various
cognitive states. This device provides data on brain activity while remaining portable
and non-invasive. This research aims to develop tools to make brain sensing more
practical for HCI and to demonstrate effective use of this cognitive state information
as supplemental input to interactive systems. First, I explored practical considerations
for using fNIRS in HCI research to determine the contexts in which fNIRS realistically
could be used. Secondly, in a series of controlled experiments, I explored cognitive
multitasking states that could be classified reliably from fNIRS data in offline
analysis. Based on these experiments, I created Brainput<\italic>, a system that
learns to identify brain activity patterns occurring during multitasking. It provides a
continuous, supplemental input stream to an interactive human-robot system, which uses
this information in real time to modify its behavior to better support multitasking.
Finally, I conducted an experiment to investigate the efficacy of
Brainput<\italic> and found improvements in performance and user
Thesis (Ph.D.)--Tufts University, 2012.
Submitted to the Dept. of Computer Science.
Advisor: Robert Jacob.
Committee: Matthias Scheutz, Remco Chang, Sergio Fantini, and Desney Tan.
Keyword: Computer science.read less