The Next Generation of Implicit Brain-Computer Interfaces: Enhancing User Learning and Creativity
Abstract: There is a
fundamentally limited bandwidth of implicit and explicit communication between the human
and computer. However, both humans and computers are complex machines, capable of
sophisticated functions. If we could provide the computer with more implicit information
about the human, without any additional effort on the part of the user, the computer
could then respond more ... read moreintelligently in return. In this thesis, I expand upon the next
generation of implicit, brain-computer interfaces by building two, real-time adaptive
brain-computer interfaces (BCIs) based on users' cognitive workload in the previously
unexplored fields of learning and creativity. I demonstrate that users can learn with
increased speed and accuracy with a BCI that guides learners' progress by measuring when
they can cognitively handle more information and providing them with the next stage of
learning at the right moment. I also build and evaluate an adaptive BCI that addresses
the no-less tricky task of increasing user creativity in a musical task. Finally, I
address the topic of whether users will actually trust such intelligent systems by
building a trust evaluation system that reveals that building trust in human-computer
interaction can be just as wiley as human-human interaction. I suggest that measuring
affective state in conjunction with cognitive state in the future would provide more
accurate insight into the user's state, allowing for more personalized and intelligent
adaptations by the next generation of implicit, brain-computer
Thesis (Ph.D.)--Tufts University, 2016.
Submitted to the Dept. of Computer Science.
Advisor: Robert Jacob.
Committee: Remco Chang, Benjamin Hescott, Paul Lehrman, and Mary Czerwinski.
Keyword: Computer science.read less
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