Neurodynamic Organization Research Articles

Parsing Neurodynamic Information Streams to Estimate the Frequency, Magnitude and Duration of Team Uncertainty.

Neurodynamic organizations are information-based abstractions, expressed in bits, of the structure of long duration EEG amplitude levels. Neurodynamic information (NI, the variable of neurodynamic organization) is thought to continually accumulate as EEG amplitudes cycle through periods of persistent activation and deactivation in response to the activities and uncertainties of teamwork. Here we show that (1) Neurodynamic information levels were a better predictor of uncertainty and novice and expert behaviors than were the EEG power levels from which NI was derived. (2) Spatial and temporal parsing of team NI from experienced submarine navigation and healthcare teams showed that it was composed of discrete peaks with durations up to 20–60 s, and identified the involvement of activated delta waves when precise motor control was needed. (3) The relationship between NI and EEG power was complex varying by brain regions, EEG frequencies, and global vs. local brain interactions. The presence of an organizational system of information that parallels the amplitude of EEG rhythms is important as it provides a greatly reduced data dimension while retaining the essential system features, i.e., linkages to higher scale behaviors that span temporal and spatial scales of teamwork. In this way the combinatorial explosion of EEG rhythmic variables at micro levels become compressed into an intermediate system of information and organization which links to macro-scale team and team member behaviors. These studies provide an avenue for understanding how complex organizations arise from the dynamics of underlying micro-scale variables. The study also has practical implications for how micro-scale variables might be better represented, both conceptually and in terms of parsimony, for training machines to recognize human behaviors that span scales of teams.

The Gap Between The Micro And Macro Scales of Teamwork is Spanned By Neural Information But Not Neural Band Power

One of the more intriguing questions about teamwork is how macro-scale behaviors arise from the micro-scale neural dynamics of each team member. In this paper we show that the low-level dynamics of EEG band power, which themselves link poorly with behavioral teamwork measures can be transformed into information-based units of neurodynamic organization that link with macro-level constructs like experience and uncertainty. We suggest it is the frequency, magnitude and durations of the temporal organizations within the EEG band power that are important for linking to macro behaviors, and not exclusively EEG power levels. These studies provide an avenue for understanding how complex organizations arise from the dynamics of underlying micro-scale variables. The study also has practical implications for how micro-scale variables might be better represented, both conceptually and in terms of parsimony, for training machines to recognize human behaviors that span scales of teamwork.

РОБОЧИЙ ПРОСТІР ПОЗНАВАЛЬНОГО ПРОЦЕСУ ЯК ПРОСТІР МЕНТАЛЬНИХ КОНСТРУКТІВ КОГНІТИВНОГО МЕХАНІЗМУ МИСЛЕННЯ

The article is dedicated the workspace of cognitive activities of the individual as a space of mental constructs of the cognitive mechanism of thinking. The relationship between of the mental constructs of cognitive space and the degree of development of cognitive abilities of the individual is studied. Thinking is considered as an unconscious process of constructing a modified model representation. The cognitive mechanism of thinking is represented as a multi-stage process of unconscious construction of the response signal in the form of a model representation of the sensory signal with consideration of information encoded in the cognitive memory space. The workspace of the thought process is considered from the perspective of modern cognitive science on modeling the activity of the neural system in the framework of the concept of neurodynamic organization and functional modes of the cognitive memory space. This approach allows us to understand the relationship between the workspace, mental constructs, the cognitive space of memory and cognitive abilities. The architecture, properties, internal mechanisms, and characteristics of the workspace are discussed. Since the workspace is formed by mental constructs, the analysis of the organization and functioning of internal mechanisms of mental constructs is performed. A dynamic instability of the workspace was found, which is caused by the dynamics of mental constructs. The study of the dynamics of mental spaces is revealed the stages of forming a response by mental constructs to a sensory signal. They can be defined as creating a mental space, creating response signal as the modified model representation, the fading of mental spaces with the display of the generated response in the cognitive memory space. Understanding thinking as a multi-stage process of "unconscious reinterpretation" of the received information can contribute to the improvement of educational technologies for the development of cognitive mechanisms of the cognitive process of the individual.

Teaching Machines to Recognize Neurodynamic Correlates of Team and Team Member Uncertainty

We describe efforts to make humans more transparent to machines by focusing on uncertainty, a concept with roots in neuronal populations that scales through social interactions. To be effective team partners, machines will need to learn why uncertainty happens, how it happens, how long it will last, and possible mitigations the machine can supply. Electroencephalography-derived measures of team neurodynamic organization were used to identify times of uncertainty in military, health care, and high school problem-solving teams. A set of neurodynamic sequences was assembled that differed in the magnitudes and durations of uncertainty with the goal of training machines to detect the onset of prolonged periods of high level uncertainty, that is, when a team might require support. Variations in uncertainty onset were identified by classifying the first 70 s of the exemplars using self-organizing maps (SOM), a machine architecture that develops a topology during training that separates closely related from desperate data. Clusters developed during training that distinguished patterns of no uncertainty, low-level and quickly resolved uncertainty, and prolonged high-level uncertainty, creating opportunities for neurodynamic-based systems that can interpret the ebbs and flows in team uncertainty and provide recommendations to the trainer or team in near real time when needed.

Advancing Our Understandings of Healthcare Team Dynamics From the Simulation Room to the Operating Room: A Neurodynamic Perspective.

The initial models of team and team member dynamics using biometric data in healthcare will likely come from simulations. But how confident are we that the simulation-derived high-resolution dynamics will reflect those of teams working with live patients? We have developed neurodynamic models of a neurosurgery team while they performed a peroneal nerve decompression surgery on a patient to approach this question. The models were constructed from EEG-derived measures that provided second-by-second estimates of the neurodynamic responses of the team and team members to task uncertainty. The anesthesiologist and two neurosurgeons developed peaks, often coordinated, of elevated neurodynamic organization during the patient preparation and surgery which were similar to those seen during simulation training, and which occurred near important episodes of the patient preparation and surgery. As the analyses moved down the neurodynamic hierarchy, and the simulation and live patient neurodynamics occurring during the intubation procedure were compared at progressively smaller time scales, differences emerged across scalp locations and EEG frequencies. The most significant was the pronounced suppression of gamma rhythms detected by the frontal scalp sensors during the live patient intubation which was absent in simulation trials of the intubation procedure. These results indicate that while profiles of the second-by-second neurodynamics of teams were similar in both the simulation and live patient environments, a deeper analysis revealed differences in the EEG frequencies and scalp locations of the signals responsible for those team dynamics. As measures of individual and team performance become more micro-scale and dynamic, and simulations become extended into virtual environments, these results argue for the need for parallel studies in live environments to validate the dynamics of cognition being observed.

Making Sense of Team Information

This paper describes how meaning can be extracted from large-scale dynamical data to make inferences about teamwork that are useful in both the theoretical and practical sense. The dynamics of an anesthesiology team are viewed from the perspectives of: 1) changes in the team’s neurodynamic organizations with large and small changes in the task; 2) how team member’s neurodynamics contribute to team neurodynamics; 3) the relationships between task events, heart-rate and neural dynamic organizations; 4) the linkages between speech flow, team and team member neurodynamics and topic discussions during Debriefing; and, 5) the micro-scale neural dynamics reflecting the involvement of the parietal lobes and gamma frequencies. These examples show how different sources of team data can contribute to multi-modal understandings of individual and teams dynamics that span micro and macro scales of teamwork.

Neuroergonomics: Quantitative Modeling of Individual, Shared, and Team Neurodynamic Information.

The aim of this study was to use the same quantitative measure and scale to directly compare the neurodynamic information/organizations of individual team members with those of the team. Team processes are difficult to separate from those of individual team members due to the lack of quantitative measures that can be applied to both process sets. Second-by-second symbolic representations were created of each team member's electroencephalographic power, and quantitative estimates of their neurodynamic organizations were calculated from the Shannon entropy of the symbolic data streams. The information in the neurodynamic data streams of health care ( n = 24), submarine navigation ( n = 12), and high school problem-solving ( n = 13) dyads was separated into the information of each team member, the information shared by team members, and the overall team information. Most of the team information was the sum of each individual's neurodynamic information. The remaining team information was shared among the team members. This shared information averaged ~15% of the individual information, with momentary levels of 1% to 80%. Continuous quantitative estimates can be made from the shared, individual, and team neurodynamic information about the contributions of different team members to the overall neurodynamic organization of a team and the neurodynamic interdependencies among the team members. Information models provide a generalizable quantitative method for separating a team's neurodynamic organization into that of individual team members and that shared among team members.

Neurodynamic and Communication Analysis of Healthcare Teams During Simulation Debriefings

Objective: To determine whether a dynamical analysis of neural and communication data streams provide fine-grained insights into healthcare team debriefings. Background: Debriefing plays a key role in experiential learning activities such as healthcare simulation because it bolsters the transfer of experience into learning through a process of reflection. There have been few studies examining the neural and communication dynamics of teams as team members are supported by trained facilitators in making better sense of their performance. Method: Electroencephalographic (EEG)–derived brain waves and speech were recorded from experienced and medical student healthcare teams during post-simulation debriefings. Quantitative estimates of the neurodynamic organizations of individual team members and the team were modeled from the EEG data streams at different scalp locations and at frequencies from 1-40 Hz. In parallel the dynamics of speech turn taking were quantified by recurrence frequency analysis. Results: Neurodynamic organizations were preferentially detected from sensors over the parietal lobes with activities present in the alpha, beta and gamma frequency bands. Rhythmic structures emerged as correlations between speech, discussion blocks and team & team member neurodynamic organizations. Conclusion: Organizational representations help reveal the neurodynamic, communication, and cognitive structures of debriefing. Application: The quantitative neurodynamic and communication measures will allow direct comparisons of debriefing structures across teams and debriefing protocols.

A Team’s Neurodynamic Organization is More than the Sum of its Members

The information within the neurodynamic data streams of teams engaged in naturalistic decision making was separated into information unique to each team member, the information shared by two or more team members, and team-specific information related to interactions with the task and team members. Most of the team information consisted of the information contained in an individual’s neurodynamic data stream. The information in an individual’s data stream that was shared with another team member was highly variable being 1-60% of the total information in another person’s data stream. From the shared, individual, and team information it becomes possible to assign quantitative values to both the neurodynamics of each team member during the task, as well as the interactions among the members of the team.

Are Neurodynamic Organizations A Fundamental Property of Teamwork?

When performing a task it is important for teams to optimize their strategies and actions to maximize value and avoid the cost of surprise. The decisions teams make sometimes have unintended consequences and they must then reorganize their thinking, roles and/or configuration into corrective structures more appropriate for the situation. In this study we ask: What are the neurodynamic properties of these reorganizations and how do they relate to the moment-by-moment, and longer, performance-outcomes of teams?. We describe an information-organization approach for detecting and quantitating the fluctuating neurodynamic organizations in teams. Neurodynamic organization is the propensity of team members to enter into prolonged (minutes) metastable neurodynamic relationships as they encounter and resolve disturbances to their normal rhythms. Team neurodynamic organizations were detected and modeled by transforming the physical units of each team member's EEG power levels into Shannon entropy-derived information units about the team's organization and synchronization. Entropy is a measure of the variability or uncertainty of information in a data stream. This physical unit to information unit transformation bridges micro level social coordination events with macro level expert observations of team behavior allowing multimodal comparisons across the neural, cognitive and behavioral time scales of teamwork. The measures included the entropy of each team member's data stream, the overall team entropy and the mutual information between dyad pairs of the team. Mutual information can be thought of as periods related to team member synchrony. Comparisons between individual entropy and mutual information levels for the dyad combinations of three-person teams provided quantitative estimates of the proportion of a person's neurodynamic organizations that represented periods of synchrony with other team members, which in aggregate provided measures of the overall degree of neurodynamic interactions of the team. We propose that increased neurodynamic organization occurs when a team's operating rhythm can no longer support the complexity of the task and the team needs to expend energy to re-organize into structures that better minimize the “surprise” in the environment. Consistent with this hypothesis, the frequency and magnitude of neurodynamic organizations were less in experienced military and healthcare teams than they were in more junior teams. Similar dynamical properties of neurodynamic organization were observed in models of the EEG data streams of military, healthcare and high school science teams suggesting that neurodynamic organization may be a common property of teamwork. The innovation of this study is the potential it raises for developing globally applicable quantitative models of team dynamics that will allow comparisons to be made across teams, tasks and training protocols.

Healthcare Teams Neurodynamically Reorganize When Resolving Uncertainty

Research on the microscale neural dynamics of social interactions has yet to be translated into improvements in the assembly, training and evaluation of teams. This is partially due to the scale of neural involvements in team activities, spanning the millisecond oscillations in individual brains to the minutes/hours performance behaviors of the team. We have used intermediate neurodynamic representations to show that healthcare teams enter persistent (50–100 s) neurodynamic states when they encounter and resolve uncertainty while managing simulated patients. Each of the second symbols was developed situating the electroencephalogram (EEG) power of each team member in the contexts of those of other team members and the task. These representations were acquired from EEG headsets with 19 recording electrodes for each of the 1–40 Hz frequencies. Estimates of the information in each symbol stream were calculated from a 60 s moving window of Shannon entropy that was updated each second, providing a quantitative neurodynamic history of the team’s performance. Neurodynamic organizations fluctuated with the task demands with increased organization (i.e., lower entropy) occurring when the team needed to resolve uncertainty. These results show that intermediate neurodynamic representations can provide a quantitative bridge between the micro and macro scales of teamwork.

Toward Objective Measures of Team Dynamics During Healthcare Simulation Training

Three-person teams of fourth-year medical students or experienced operating room practitioners performed simulations around the construct of ventilation. Team member communications together with EEG-derived brainwaves were collected and classified each second and the changing neurodynamic as well as communication organizations of the team were modeled. The fluctuating neurodynamic organizations were obtained from symbolic representations of the EEG power levels of team members while changes in communication were determined by Latent Semantic analysis – derived measures of communication content. The neurodynamic organizations of the teams at the 10 Hz (alpha) and 39 Hz (gamma) EEG frequencies fluctuated with task demands. The frequency, magnitudes, and durations of these fluctuations differed between novice and expert teams, and these changes in the team’s neurodynamic organizations were paralleled by dynamic changes in communication and improvements in TeamSTEPPS® ratings. Neurodynamic and communication measures of team organization may therefore be valuable tools for understanding and assessing the short term dynamics of teams during simulation training, complementing and extending observational evaluations of teams.

Modeling the neurodynamic organizations and interactions of teams

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.

Toward a quantitative description of the neurodynamic organizations of teams

The goal was to develop quantitative models of the neurodynamic organizations of teams that could be used for comparing performance within and across teams and sessions. A symbolic modeling system was developed, where raw electroencephalography (EEG) signals from dyads were first transformed into second-by-second estimates of the cognitive Workload or Engagement of each person and transformed again into symbols representing the aggregated levels of the team. The resulting neurodynamic symbol streams had a persistent structure and contained segments of differential symbol expression. The quantitative Shannon entropy changes during these periods were related to speech, performance, and team responses to task changes.The dyads in an unscripted map navigation task (Human Communication Research Centre (HCRC) Map Task (MT)) developed fluctuating dynamics for Workload and Engagement, as they established their teamwork rhythms, and these were disrupted by external changes to the task. The entropy fluctuations during these disruptions differed in frequency, magnitude, and duration, and were associated with qualitative and quantitative changes in team organization and performance. These results indicate that neurodynamic models may be reliable, sensitive, and valid indicators of the changing neurodynamics of teams around which standardized quantitative models can begin to be developed.

Towards the Development of Quantitative Descriptions of the Neurodynamic Rhythms and Organizations of Teams

The goal was to begin developing quantitative approaches for modeling the neurodynamic rhythms and organizations of teams. Raw EEG signals from team members were first transformed into es-timates of cognitive workload and transformed again into neurodynamics symbols showing the second-by-second workload of each individual as well as the team. Periods of increased or decreased symbol organiza-tion in the data streams were hypothesized to reflect periods of increased or decreased organization around the cognitive construct of workload. These segments were identified by a moving average smoothing of the Shannon entropy over the length of the performance and then related to team speech, actions and team responses to endogeneous and exogeneous task changes. Two-person teams in an unscripted map naviga-tion task developed a common, dominant coordination dynamic for workload whose rhythm was disrupted by exogeneous changes to the task. The entropy fluctuations during these disruptions differed in magnitude and duration within and across performances and were associated with qualitative and quantitative changes in team organization. Similar results were obtained with three and six person teams on other complex tasks. These results indicate that neurodynamic measures may be reliable, sensitive and valid indicators of the changing neurodynamics of teams around which standardized quantitative models can be developed.

On the intercorrelation of some frequency and amplitude parameters of the human EEG and its functional significance. Communication I: Multidimensional neurodynamic organization of functional states of the brain during intellectual, perceptive and motor activity in normal subjects

In 95 normal subjects, a separate evaluation of the amplitude and frequency parameters of EEG by period analysis made it possible to reveal, using factor analysis, four independent groups of parameters — the EEG factors, two of which being independent of the amplitude fluctuations. They were considered as integral EEG characteristics of qualitatively different neurophysiological processes. Decrease of Factor I values during mental activity (called `general activation') reflected an intercorrelated desynchronization of the wave amplitudes in all the bands, a decrease of α-index (percentage presence in epoch) and regularity together with parallel increase of the indices and mean periods of Δ- and θ-waves. This generalized reaction has shown `non-specific' dependence upon novelty and difficulty of the tasks and stimuli with certain task-specific topographical distribution. An increase of values of regional Factor Ia in the anterior areas was caused by Δ- and θ-amplitude synchronization, more pronounced during matching the rhymes (MR) than in mental multiplication (MM). An increase of Factor II values (related to increase of the index, frequency and regularity of β-activity and called `cortical excitation' , CE) was more expressed during MR, whereas an increase of Factor III values (an increase of mean α-period and θ-index called `active selective inhibition', ASI) was characteristic of MM, the latter reaction being evident in the right hemisphere. During analysis of external sound stimuli and rhythmical clenching of a fist, an increase of Factor III values was accompanied by decrease of Factor II values in the motor activity; such reciprocal reaction being localized in the central areas contralateral to the hand moved. Neuropsychological analysis suggests that CE correlates with associative and successively organized mental operations involving search for memory traces and ASI presumably relates to different aspects of mental selectivity such as simultaneous mental operations, voluntary attention and mental automation, the latter two cases being supported by parallel reduction of CE.

Neurodynamic organization of different types of cognitive and motor activity

Neurodynamic organization of different types of cognitive and motor activity

TECHNIQUE AND EVALUATION OF THE ELECTROENCEPHALOGRAM

ArticlesTECHNIQUE AND EVALUATION OF THE ELECTROENCEPHALOGRAMPauline A. DavisPauline A. DavisPublished Online:01 Jan 1941https://doi.org/10.1152/jn.1941.4.1.92MoreSectionsPDF (3 MB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat Previous Back to Top Next Download PDF FiguresReferencesRelatedInformation Cited ByMeanings of Waves: Electroencephalography and Society in Mexico City, 1940–195012 January 2017 | Science in Context, Vol. 29, No. 4The relationship of theory and methodology in EEG studies of mental activityInternational Journal of Psychophysiology, Vol. 62, No. 3On the intercorrelation of some frequency and amplitude parameters of the human EEG and its functional significance. Communication I: Multidimensional neurodynamic organization of functional states of the brain during intellectual, perceptive and motor activity in normal subjectsInternational Journal of Psychophysiology, Vol. 28, No. 1Age-Related Morphological Changes in Auditory Middle-Latency ResponseInternational Journal of Audiology, Vol. 26, No. 5Age development and sex differences of various EEG elements in healthy children and adults — Quantification by a computerized wave form recognition methodElectroencephalography and Clinical Neurophysiology, Vol. 60, No. 5Frequency analysis of the resting awake EEG in mild senile dementia of Alzheimer typeElectroencephalography and Clinical Neurophysiology, Vol. 55, No. 4EEG Signal AnalysisElectroencephalographic studies in toad (Bufo melanostictus) following prolonged exposure to heat during hibernation and non-hibernationExperientia, Vol. 29, No. 12CHANGES IN THE ELECTROENCEPHALOGRAM FREQUENCY SPECTRUM DURING THE PRESENTATION OF NEUTRAL AND MEANINGFUL STIMULI TO PATIENTS WITH LESIONS OF THE FRONTAL LOBESEEG Assessment in Clinical MedicineEEGs in Chronic Schizophrenia29 January 2018 | British Journal of Psychiatry, Vol. 116, No. 533VIII: Clinical-Electroencephalographic investigationActa Psychiatrica Scandinavica, Vol. 41ELECTROENCEPHALOGRAPHIC PATTERN QUANTIFICATION AND THE AROUSAL CONIINIUMPsychophysiology, Vol. 2, No. 2Computer Techniques for the Study of Patterns in the ElectroencephalogramIRE Transactions on Bio-Medical Electronics, Vol. 9, No. 1Klinische ElektrencephalographieThe electroencephalogram in a group of normal subjects at sea level and at 14,900 feetElectroencephalography and Clinical Neurophysiology, Vol. 10, No. 1A Clinical Trial of Reserpine in the Treatment of Anxiety8 February 2018 | Journal of Mental Science, Vol. 102, No. 426A method of statistical treatment for electronically analysed EEG dataElectroencephalography and Clinical Neurophysiology, Vol. 6Neurophysiologische UntersuchungsmethodenElectroencephalographic findings in acute nephritisThe Journal of Pediatrics, Vol. 36, No. 4Standardized Pain Stimulation as Controlled Stress in Physiological Studies of PsychoneurosisScience, Vol. 108, No. 2810ELECTRO-ENCEPHALOGRAPHIC CHANGES IN MIGRAINE REVIEW OF 51 CASESThe Lancet, Vol. 250, No. 6463Electroencephalographic Studies After Ligation of the Internal Carotid ArteryJournal of Neurosurgery, Vol. 4, No. 1Electro-Encephalographic Examinations of Imbeciles..Acta Paediatrica, Vol. 32, No. 3-4ON THE CLINICAL APPLICATION OF ELECTRO-ENCEPHALOGRAPHY WITH A DESCRIPTION OF A NEW ELECTROENCEPHALOGRAPHActa Psychiatrica Scandinavica, Vol. 18, No. 5The relation of cerebral dysrhythmia to eclampsiaAmerican Journal of Obstetrics and Gynecology, Vol. 45, No. 6Electroencephalography: Its applications in neurology and psychiatryThe Psychiatric Quarterly, Vol. 15, No. 3THE SIGNIFICANCE OF AN ABNORMAL ELECTROENCEPHALOGRAMJournal of Neurology, Neurosurgery & Psychiatry, Vol. 4, No. 3-4 More from this issue > Volume 4Issue 1January 1941Pages 92-114 https://doi.org/10.1152/jn.1941.4.1.92History Published online 1 January 1941 Published in print 1 January 1941 Metrics