-------- Forwarded Message -------- Subject: [AISWorld] Call For Participation: IEEE COMPSAC - Big Data Hackathon Date: Sun, 8 Jul 2018 17:05:19 +0000 From: Hossain Shahriar hshahria@kennesaw.edu

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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