Group led by DCS was awarded an increase to an agreement with the Cognition and Neuroergonomics Collaborative Technology Alliance (CaN CTA)
ABERDEEN PROVING GROUNDS, MD – A DCS Corp (DCS)-led consortium was awarded a $15 million ceiling increase to the Cognition and Neuroergonomics Collaborative Technology Alliance (CaN CTA) cooperative agreement to expand fundamental research efforts in computational neuroscience for the United States Army. This brings the total potential value of this cooperative agreement to $65 million. DCS is also the prime contractor on the companion contract for technology transition with a potential total value of $80 million.
Under this cooperative agreement, DCS leads a team composed of fourteen academic institutions and 3 industry partners. The academic partners include prominent U.S. research institutions such as University of California- San Diego, Columbia University, Carnegie Mellon University, and University of Pennsylvania, as well as leading institutions in three other countries. Industry partners include two start-ups that were created by researchers from the CTA and DCS.
The CaN CTA program started in 2010 for the development and demonstration of fundamental translational principles that govern the application of neuroscience-based research and theory to complex operational settings. Some of the key research themes in this program relate to the investigation of approaches to overcome the traditional restrictive paradigms of neuro-psychology research and the advancement of capabilities to record behavior and the environment in sufficient detail and across a sufficient breadth of circumstances with multiple sensing modalities so that relationships between the variations in environment, behavior and functional brain dynamics could be explored. To this end, DCS has been contributing significantly to the advancement of wearable, mobile sensing technology, to the engineering and system architecture for data acquisition and storage, and to the signal processing, statistical analytics, and machine learning techniques to explore and exploit neuro-physiological data streams.
DCS offers advanced technology and management solutions to Government agencies in the national security sector. The transformative ideas and entrepreneurial spirit that characterize our 1,000-plus employee-owners allow DCS to ensure the success of each client’s mission and actively contribute to the well-being of the Nation. For more information about the work we do for the U.S. Army, please visit: http://18.104.22.168/our-customers/us-army/.
For more information on the work of DCS scientists and engineers, please refer to these publications and papers:
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)