DCS and Army Research Lab team win international machine learning competition

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Mar 27, 2018, 08:17 ET

ABERDEEN PROVING GROUND, Md., March 27, 2018 /PRNewswire-USNewswire/ -- A joint effort between the U.S. Army Research Laboratory and DCS Corporation recently won the Neurally Augmented Image Labelling Strategies, or NAILS challenge, at an international machine learning research competition in Tokyo.

Dr. Vernon Lawhern, ARL mathematical statistician and Amelia J. Solon, DCS scientist, led the joint research project. Other team members included Drs. Brent Lance, ARL and Stephen Gordon, DCS.

The team presented its paper, Deep Learning Approaches for P300 Classification in Image Triage: Applications to the NAILS Task, at the 13th National Institute of Testbeds and Community Information Access Research Conference on evaluation of information.

Teams participating in the NAILS challenge developed machine learning methods to detect – through brain activity – whether an image that a person was seeing was a task-relevant image or not. In performing the research, the researchers calibrated an in-house tool called EEGNet, which is a deep convolutional network capable of learning robust representations of specific brain responses using relatively sparse training sets. Using this approach, they trained a unique instantiation of EEGNet for each subject and subsequently obtained the highest classification performance, averaged across all subjects, of the participating teams.

"EEGNet allows researchers to train models for different neural responses using examples of those responses collected under a wide variety of conditions and from multiple individuals," Gordon said. "In this way, EEGNet provides both a 'common framework' for analyzing disparate data sets as well as a tool for extrapolating results from simplified to more complex domains."

The team participated in conference discussions focused on the state of brain computer interface technology and how it can be leveraged for information retrieval applications; future directions for the NAILS task; assessing models with IR oriented evaluation metrics, and encouraging the development of general BCI algorithms that are not calibrated per-subject or task and hold greater potential for measuring human state in complex, real-world, environments.

"This work is part of a larger research program at ARL that focuses on understanding the principles that govern the application of neuroscience-based research to complex operational settings," Lawhern said. "By competing in this competition we were able to showcase our expertise in this area to the broader scientific community. Ultimately, we are interested in using neuroscience-based approaches to develop human-computer interaction technologies that can adapt to the state of the user."

The researchers conducted the work as part of DCS's Cognition and Neuroergonomics Collaborative Technology Alliance contract with ARL. The CaN CTA is the U.S. Army's flagship basic science research and technology transition program in the neurosciences.

More information on the winning research is available on Google Scholar Citations.

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