Neural signatures of team coordination are revealed by multifractal analysis

Abstract

The quality of a team depends on its ability to deliver information through a hierarchy of team members and negotiate processes spanning different time scales. That structure and the behavior that results from it pose problems for researchers because multiply-nested interactions are not easily separated. We explored the behavior of a six-person team engaged in a Submarine Piloting and Navigation (SPAN) task using the tools of dynamical systems. The data were a single entropy time series that showed the distribution of activity across six team members, as recorded by nine-channel electroencephalography (EEG). A single team’s data were analyzed for the purposes of illustrating the utility of multifractal analysis and allowing for in-depth exploratory analysis of temporal characteristics. Could the meaningful events experienced by one of these teams be captured using multifractal analysis, a dynamical systems tool that is specifically designed to extract patterns across levels of analysis? Results indicate that nested patterns of team activity can be identified from neural data streams, including both routine and novel events. The novelty of this tool is the ability to identify social patterns from the brain activity of individuals in the social interaction. Implications for application and future directions of this research are discussed.

Keywords:

• Team coordination
• Multifractal
• EEG
• Complex systems

This work was supported in part by the ASU College of Liberal Arts and Sciences Security and Defense Initiative Fellowship awarded to A. Likens; National Science Foundation [BCS-1255922] awarded to P. Amazeen, and [BCS-1257112] awarded to J. Gorman; NSF SBIR [IIP 0822020], [IIP 1215327] awarded to R. Stevens; the Office of Naval Research; and the Defense Advanced Research Projects Agency under contract number [W31P4Q12C0166]. The views, opinions, and/or findings contained are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense.

Notes

¹ The 100-second window preserved critical characteristics of the time series. Windows larger than 100-seconds decreased the resolution of entropy changes, and substantially smaller windows (e.g., 30 seconds) introduced meaningless fluctuations. For these categorical data, entropy ranged in value from log₂ (1) = 0 to log₂ (25) ~ 4.64.