Harvard Graduate School of Design | Fall 2017
Deployable Surfaces: Dynamic performance through multi-material architectures
Team | Kevin Chong , Eliza Pertigkiozoglou, Carla Saad, Anne Stack
Instructors | Chuck Hoberman, Jonathan Grinham
Harvard Graduate School of Design | Fall 2017
Deployable Surfaces: Dynamic performance through multi-material architectures
Team | Kevin Chong , Eliza Pertigkiozoglou, Carla Saad, Anne Stack
Instructors | Chuck Hoberman, Jonathan Grinham
Harvard Graduate School of Design | Fall 2017
Deployable Surfaces: Dynamic performance through multi-material architectures
Team | Kevin Chong , Eliza Pertigkiozoglou, Carla Saad, Anne Stack
Instructors | Chuck Hoberman, Jonathan Grinham
NODE
​
reimagining emergency
​
response for resilient
cities
NODE
​
reimagining emergency
​
response for resilient
cities
NODE
​
reimagining emergency
​
response for resilient
cities
EEG DECISIONS
Visualizing Human Decision Making
Under Uncertainty
Overview.
EEGDecisions is a visualization tool to help neuroscientists correlate brain activity with the geo-location of a studied subject.
Team
Advisors
Dianne Lee
Phoebe Lin
Carla Saad
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Johanna Beyer
Michael Behrisch
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Research Paper
Assets
Prototype here
in collaboration with Harvard’s Computational Cognitive Neuroscience lab
How does the brain make decisions under uncertainty in the real world?
Decision-making has long been regarded as a difficult and complex process. As we go about our daily lives, we are confronted with many decisions and their consequences, which we translate into usable information for similar situations in the future. A single decision can be viewed as an individual problem called the explore-exploit dilemma. The explore-exploit dilemma originates from the tradeoffs associated with selecting a decision of known value versus selecting alternative decisions of uncertain values.
Framework.
EEGDecisions is a novel visualization framework designed for detailed analysis of the real-time neural activity in combination with decisions made in a naturalistic setting. Identifying specific waveforms and peaks in different EEG channels will be crucial for understanding the brain and how decisions are evaluated under various uncertainty levels.
How might we visualize EEG data variation in relation to geospatial data?
A visualization to help correlate the brain activity with the geo-location of the body
Electric activity in the human brain
Geolocation of the human body
Persona.
Users: Neuroscientists
In order to understand the needs of our users, we conducted several interviews to discuss their goals, pain- points, and needs.
Process.
Previous Interface
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Our Collaborators were using a previous interface to do their studies.
There were many elements that led to information clutter and cognitive load.
Our approach focuses on 2 main elements of the interface: the map and the corresponding EEG data graphs
Evolution of the interface design from low resolution sketches to a built product
From Low-fidelity to medium-fidelity
3 Main Features
After going through several iterations and facing multiple implementation challenges related to the structure of data, we focused on 3 main functionality.
GPS Data
Data from Muse
Challenges
Large Quantity of Data
Timestamp Inaccuracy for location
Timestamp mismatch across EEG & location
Solutions
User-Induced Selection
Interpolation of Values
Before & After Buffer
Demo of the built prototype
Information Architecture
EEGDecision's Interface
Key Takeaways
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Sometimes design needs to be rethought in relation to technical implementation especially when designing for data.
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Conducting frequent usability studies and validation sessions helps shape a more viable and useful product.