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Call for Hackathon Participation [winners: Cash Awards]
IEEE Big Data Governance & Metadata Management: Brain Data
Bank on Video Gaming Enhances Cognitive Skills (Part of COMPSAC
Conference, July 23 - 27, 2018)
National Institute of Informatics, Tokyo, Japan, July 23 - 24,
2018
Participants -Come and join us (training available, no prior
knowledge is needed)!
We need teams (3-4 members per team) of data scientists, computer
scientists, engineers, statisticians, analysts, problem solvers,
etc. to explore new patterns or knowledge from the given datasets.
Problem Statement
Cognitive control is defined by a set of neural processes that
allow us to interact with our complex environment in a goal
directed manner. Humans regularly challenge these control
processes when attempting to simultaneously accomplish multiple
goals (multitasking). It is clear that multitasking behavior has
become ubiquitous in today's technologically dense world, and
substantial evidence has accrued regarding multitasking
difficulties and cognitive control deficits in our aging
population.
Here we show that multitasking performance, as assessed with a
custom-designed three-dimensional video game (NeuroRacer),
exhibits a linear age-related decline from 20 to 79 years of age.
By playing an adaptive version of NeuroRacer in multitasking
training mode, older adults (60 to 85 years old) reduced
multitasking costs compared to both an active control group and a
no-contact control group, attaining levels beyond those achieved
by untrained 20-year-old participants, with gains persisting for 6
months.
These findings highlight the robust plasticity of the prefrontal
cognitive control system in the aging brain, and provide the first
evidence, to our knowledge, of how a custom-designed video game
can be used to assess cognitive abilities across the lifespan,
evaluate underlying neural mechanisms, and serve as a powerful
tool for cognitive enhancement.
Tutorial and Hands-on (no neuroscience background is needed but
willing to work within a team is preferred)
Dr. David Ziegler (Tutorial), Director of Technology Program,
Multimodal Biosensing, UCSF, USA
Dr. Seth Elkin-Frankston (Hands-on), Scientist, Cognitive Systems,
Charles River Analytics Inc., USA
Challenging Questions
- Try to conduct an event-related potential (ERP) analysis of the
data in one or more conditions. How does this approach compare to
that used in the Nature paper (i.e., ERSP-Event-Related Spectral
Perturbation or time-frequency analysis)? Hint: check out the
EEGLab and Fieldtrip tutorial
- Try conducting an independent component analysis (ICA)
decomposition analysis of the data (Hint: this is best done in
EEGLab). How does this approach compare to that used in the Nature
paper or the ERP analysis suggested above? What new information
can we learn using this approach?
- Would a micro-state analysis be appropriate for the data? What
new knowledge might we learn from such an approach?
- What advanced methods (e.g., deep learning, but also others) are
available that would help predict post game performance?
Specifically by what mechanisms and by how much?
Important Dates / Websites / Point of Contact
July 16, 2018: Deadline for hackathon sign-up
Oct. 23, 2018: Due date for hackathon implementation write-up (to
be published under IEEE BDGMM site)
COMPSAC:
https://ieeecompsac.computer.org/2018/
Hackathon:
https://ieeecompsac.computer.org/2018/hackathon
Datasets at IEEE DataPort: Sample Datasets (330MB), Full Datasets
(17GB, simple registration is required)
IEEE BDGMM:
https://ieeesa.io/bdgmm
Wo Chang,
wchang@nist.gov, Chair of IEEE BDGMM, NIST, USA
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