Publications & Research
For over a decade, the Advanced Brain Monitoring team has led or partnered in clinical research studies resulting in over 50 publications. In the past year, our strong and growing technical team of PhD- and masters-level hardware engineers, biostatisticians, software engineers and clinical study design experts added three more peer-reviewed findings published in internationally accredited journals to our library. While the study topics and outcomes are wide ranging, the results of each were driven by ABM developed technology including the B-Alert wireless-EEG systems.
Stevens, R., Berka, C., Galloway, T., Wang, P., and Behneman, A. (2011). Developing Systems for the Rapid Modeling of Team Neurodynamics. Interservice/Industry Training, Simulation, and Education Conference, Orlando, FL.
Abstract:
Cognitive Neurophysiologic synchronies (NS) are a low level data stream derived from EEG measurements that can be collected and analyzed in near real time and in realistic settings. We are using NS to develop systems that can rapidly determine the functional status of a team with the goals of being able to assess the quality of a teams’ performance / decisions, and to adaptively rearrange the team or task components to better optimize the team. EEG-derived measures of engagement from Submarine Piloting and Navigation team members were normalized and pattern classified by self-organizing artificial neural networks and hidden Markov models. The temporal expression of these patterns were mapped onto team events and related to the frequency of team members’ speech. Standardized models were created using pooled data from multiple teams and were used to compare NS expression across teams, training sessions and levels of expertise. These models have also been incorporated into software systems that can provide for rapid (minutes) after training feedback to the team and provide a framework for future real-time monitoring.
Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model.
Johnson, R.R., Popovic, D.P., Olmstead, R.E., Stikic M., Levendowski, D.J., and Berka, C. (2011). Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model. Biological Psychology 87(2): p. 241-50.
Abstract:
A great deal of research over the last century has focused on drowsiness/alertness detection, as fatigue-related physical and cognitive impairments pose a serious risk to public health and safety. Available drowsiness/alertness detection solutions are unsatisfactory for a number of reasons: (1) lack of generalizability, (2) failure to address individual variability in generalized models, and/or (3) lack of a portable, un-tethered application. The current study aimed to address these issues, and determine if an individualized electroencephalography (EEG) based algorithm could be defined to track performance decrements associated with sleep loss, as this is the first step in developing a field deployable drowsiness/alertness detection system. The results indicated that an EEG-based algorithm, individualized using a series of brief “identification” tasks, was able to effectively track performance decrements associated with sleep deprivation. Future development will address the need for the algorithm to predict performance decrements due to sleep loss, and provide field applicability.
EEG-derived estimators of present and future cognitive performance.
Stikic M, Johnson RR, Levendowski DJ, Popovic DP, Olmstead RE and Berka C. (2011). EEG-derived estimators of present and future cognitive performance. Frontiers in Human Neuroscience. 5: 70.
Abstract:
Previous EEG-based fatigue-related research primarily focused on the association between concurrent cognitive performance and time-locked physiology. The goal of this study was to investigate the capability of EEG to assess the impact of fatigue on both present and future cognitive performance during a 20min sustained attention task, the 3-Choice Active Vigilance Task (3CVT), that requires subjects to discriminate one primary target from two secondary non-target geometric shapes. The current study demonstrated the ability of EEG to estimate not only present, but also future cognitive performance, utilizing a single, combined reaction time and accuracy performance metric. The correlations between observed and estimated performance, for both present and future performance, were strong (up to 0.89 and 0.79, respectively). The models were able to consistently estimate “unacceptable” performance throughout the entire 3CVT, i.e., excessively missed responses and/or slow reaction times, while acceptable performance was recognized less accurately later in the task. The developed models were trained on a relatively large dataset (n=50 subjects) to increase stability. Cross-validation results suggested the models were not over-fitted. This study indicates that EEG can be used to predict gross-performance degradations 5 to 15min in advance.
Interactive Neuro-Educational Technologies (I-NET): Development of a Novel Platform for Neurogaming.
Raphael, G., Behneman, A., Tan, V., Pojman, N., & Berka, C. (2011). Interactive Neuro-Educational Technologies (I-NET): Development of a Novel Platform for Neurogaming. Paper presented at the Human-Computer Interaction International, Orlando, FL.
Abstract:
The advances in sophisticated, immersive and highly engaging video gaming technology have resulted in the introduction of “serious gaming” as platforms for training. A virtual environment that mimics reality as closely as possible is an effective instructional medium and also serves as a performance improvement/evaluation platform. However, the current methodologies suffer from several limitations: 1) conventional qualitative evaluation techniques that are removed from the trainee?s actual experience in both time and context 2) open loop platforms fail to support adaptive training and scenarios or leverage repeatability to accelerate training 3) failure to adapt to individual?s current psychophysiological state, limiting skill acquisition rates 4) multi-person tasks that lack tools for objective assessment and prediction of team cohesion or performance. As part of our initiative to invent a suite of Interactive Neuro-Educational Technologies (I-NET), we have developed a Neurogaming platform that will help resolve many of these limitations.
Linking Models of Team Neurophysiologic Synchronies for Engagement and Workload with Measures of Team Communication.
Stevens, R., Berka, C., Galloway, T., and Behneman, A. (2011). Linking Models of Team Neurophysiologic Synchronies for Engagement and Workload with Measures of Team Communication. Proceedings of the Behavior Representation in Modeling Simulation (BRIMS) Conference, Sundance, UT.
Abstract:
Neurophysiologic synchronies (NS) are a low level data stream derived from EEG measurements that can be collected and analyzed in near real time and in realistic settings. The objective of this study was to compare the simultaneous expression of NS specific for Engagement (NS_E) and Workload (NS_WL) and to relate them to the frequency of conversation between team members during Submarine Piloting and Navigation (SPAN) simulations. If the expression of different NS patterns is sensitive to changes in the behavior of teams over time they may be an additional useful tool for modeling team cognition. EEG-derived measures of Engagement and Workload from SPAN team members were normalized and pattern classified by self-organizing artificial neural networks and hidden Markov models. The temporal expression of these patterns were mapped onto team events and related to the frequency of conversation among team members. The modeling was performed with pooled data from multiple teams to facilitate comparisons across teams and levels of expertise, and to provide a framework for real-time monitoring of team performance. NS_E and NS_WL showed different dynamics across the task and also in response to team communications. These studies indicate that expression of different neurophysiologic indicators measured by EEG may complement rather than duplicate communication metrics as measures of team cognition. The development of generic NS models also provides a framework for real-time cognitive monitoring of teams.
Mapping Neurophysiologic Synchrony Attractor States and Entropy Fluctuations during Submarine Piloting and Navigation.
Stevens, R., Berka, C., Galloway, T., and Wang, P. (2011). NeuroGaming: Mapping Neurophysiologic Synchrony Attractor States and Entropy Fluctuations during Submarine Piloting and Navigation. Paper presented at the Applied Human Factors and Ergonomics Society (HFES), Las Vegas, NV.
Abstract:
Our objective was to apply ideas from complexity theory to derive expanded models of Submarine Piloting and Navigation (SPAN) showing how teams cognitively respond to task changes and how this was altered with experience. The cognitive measure highlighted was an electroencephalography (EEG)-derived measure of engagement (EEG-E) that was modeled into a collective team variable termed neurophysiologic synchronies of engagement (NS_E) thus showing the engagement of each of 6 team members as well as the engagement of the team as a whole. We show that the dominant NS_E patterns were different for novice and experienced teams, and that experienced teams used a larger repertoire of potential NS_E patterns. Estimates of the Shannon entropy of the NS_E data streams provided a quantitative history of NS_E fluctuations which were associated with the efficiency of the SPAN teams in updating the ship’s position.