Modeling the neurodynamic organizations and interactions of teams

Abstract

Across-brain neurodynamic organizations arise when teams perform coordinated tasks. We describe a symbolic electroencephalographic (EEG) approach that identifies when team neurodynamic organizations occur and demonstrate its utility with scientific problem solving and submarine navigation tasks. Each second, neurodynamic symbols (NS) were created showing the 1–40 Hz EEG power spectral densities for each team member. These data streams contained a performance history of the team’s across-brain neurodynamic organizations. The degree of neurodynamic organization was calculated each second from a moving window average of the Shannon entropy over the task. Decreased NS entropy (i.e., greater neurodynamic organization) was prominent in the ~16 Hz EEG bins during problem solving, while during submarine navigation, the maximum NS entropy decreases were ~10 Hz and were associated with establishing the ship’s location. Decreased NS entropy also occurred in the 20–40 Hz bins of both teams and was associated with uncertainty or stress. The highest mutual information levels, calculated from the EEG values of team dyads, were associated with decreased NS entropy, suggesting a link between these two measures. These studies show entropy and mutual information mapping of symbolic EEG data streams from teams can be useful for identifying organized across-brain team activation patterns.

Keywords:

• Team neurodynamics
• Synchrony
• EEG
• Entropy
• Mutual information teamwork

The authors thank Marcia Sprang, Ph.D., and the students at Esperanza High School for their assistance with the Map Task studies. The authors also thank Veasna Tan and Bradly Stone from Advanced Brain Monitoring, Inc. for their assistance with EEG data collection and processing. Special thanks to Thomas Wohlgemuth and Lt. Robert Buckles at the Submarine Learning Center in Groton, CT, for their help with the on-site logistics for the SPAN studies.

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported in part by the Defense Advanced Projects Agency [contract number W31P4Q-12-C-0166] and National Science Foundation SBIR [grant numbers IIP-1215, IIP-121527, IIP 0822020, and IIP 1215327].