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Neuroergonomics pp 251–269Cite as

Approaches for Inserting Neurodynamics into the Training of Healthcare Teams

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Abstract

Team neurodynamics is the study of the changing rhythms and organizations of teams from the perspective of neurophysiology. As a discipline, team neurodynamics is located at the intersection of collaborative learning, psychometrics, complexity theory, and neurobiology with the resulting principles and applications both drawing from and contributing to these specialties. This article describes the tools for studying team neurodynamics and illustrates the potential and the challenges these methods and models have for better understanding healthcare team training and performance. The fundamental metric is neurodynamic organization, which is the tendency of teams and its members to enter into prolonged metastable relationships when they experience and resolve uncertainty. The patterns of these relationships are resolved by symbolic modeling of electroencephalographic (EEG) power levels of the team members, and the information in these patterns are calculated using information theory tools. The topics discussed in this chapter anticipate the time when dynamic biometric data can contribute to our understanding of how to rapidly determine a team’s functional status, and how to use this information to optimize outcomes and training. The rapid, dynamic, and task neutral measures make the lessons learned in healthcare applicable to other complex group and team environments, and provide a foundation for incorporating these models into machines to support the training and performance of teams.

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Author information

Authors and Affiliations

  1. UCLA School of Medicine, Brain Research Institute, Los Angeles, CA, USA

    Ronald Stevens

  2. The Learning Chameleon, Inc., Culver City, CA, 90230, USA

    Ronald Stevens & Trysha Galloway

  3. JUMP Simulation and Education Center, Peoria, IL, USA

    Ann Willemsen-Dunlap

Authors
  1. Ronald Stevens
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  2. Trysha Galloway
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  3. Ann Willemsen-Dunlap
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Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA

    Chang S. Nam

Appendix 1: Processing EEG Data Streams

Appendix 1: Processing EEG Data Streams

The data acquisitions began shortly after the EEG sensors were adjusted for good contact (<10 Ω). The EEG data streams were aligned for the three team members using electronic markers inserted into the EEG data streams as well as the events observed in videos. Signals from outside the brain can be a confounder when interpreting models built from EEG signals, especially signals obtained in complex environments. Commonly found artifacts are generated from speech, eyeblinks, heartbeats, breathing rhythms and other electromyography sources. As neurodynamic organizations regularly occur during silence, speech is an unlikely source for most organizations (Stevens & Galloway, 2014). EEG processing included separate high and low bandpass filters, the rejection of bad channels and regular rhythms associated with eyeblinks and heartbeats were identified and removed during data preprocessing (Delorme & Makeig, 2004; Delorme et al., 2012) by the interactive Matlab® toolboxes EEGLAB and FieldTrip (oostenveld, Fries, Maris, & Schoffelen, 2011).

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Stevens, R., Galloway, T., Willemsen-Dunlap, A. (2020). Approaches for Inserting Neurodynamics into the Training of Healthcare Teams. In: Nam, C. (eds) Neuroergonomics. Cognitive Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-34784-0_13

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