By partnering with government, industry and prominent academic institutions, our scientists and engineers have been integral to the advancements in basic and applied research

As a leader in advanced technology and management solutions for the government, DCS makes it a priority to stay on the cutting edge of the research and development that drives these solutions. By partnering with government, industry and prominent academic institutions, our scientists and engineers have been integral to the advancements in basic and applied research on a wide range of topics with many publications on respected journals. In many cases, they challenge existing norms to develop alternative methods of approaching issues and achieve innovative solutions.

Consider the fascinating and rapidly growing field of research surrounding Brain Computer Interface (BCI) technology, for example. BCI technology has tremendous utility for both biomedical applications, such as neuroprosthetic control, and non-medical applications. Several of our employees have been working with our government and academic partners to improve understanding of brain signals used in BCI through advanced machine learning approaches so that more robust applications can be developed.

The following research papers provide just a sample of what DCS employee-led research has produced on these neuroscientific topics:

S. Gordon, et al. (2017). “Real World BCI: Cross-Domain Learning and Practical Applications”. BCIforReal’17, March 13, 2017, Limassol, Cyprus http://www.doi.org/index.html.

Gordon, S. M., Lawhern, V., Passaro, A. D., & McDowell, K. (2015). Informed decomposition of electroencephalographic data. Journal of Neuroscience Methods, 256, 41–55. https://doi.org/10.1016/j.jneumeth.2015.08.019

Gordon, S. M., McDaniel, J. R., Metcalfe, J. S., & Passaro, A. D. (2015). Using Behavioral Information to Contextualize BCI Performance. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition (Vol. 9183, pp. 211–220). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-20816-9_21

Kellihan, B., Doty, T. J., Hairston, W. D., Canady, J., Whitaker, K. W., Lin, C.-T., … McDowell, K. (2013). A Real-World Neuroimaging System to Evaluate Stress. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition (Vol. 8027, pp. 316–325). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-39454-6_33

Metcalfe, J. S., Gordon, S. M., Passaro, A. D., Kellihan, B., & Oie, K. S. (2015). Towards a Translational Method for Studying the Influence of Motivational and Affective Variables on Performance During Human-Computer Interactions. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition (Vol. 9183, pp. 63–72). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-20816-9_7

Nonte, M. W., Hairston, W. D., & Gordon, S. M. (2016). Comparing EEG Artifact Detection Methods for Real-World BCI. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience: 10th International Conference, AC 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016, Proceedings, Part I (pp. 91–101). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-39955-3_9

Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2016). EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces. arXiv Preprint arXiv:1611.08024. Retrieved from https://arxiv.org/abs/1611.08024 (in review)


By: Tim Lee

Vice President, Intelligent Systems

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Carrie, Senior Technical Recruiter at DCS