Chris Berka

Event-related potentials during selective attention to speech sounds.

Event-related potentials (ERPs) were recorded from subjects while they selectively attended to sequences of stop-consonant syllables or sequences of tone pips of different frequencies. The ERP difference waveforms that distinguished attended from unattended speech sounds were highly similar in morphology and scalp distribution to the difference waveforms elicited by the tone pips. These results suggest that the attention mechanisms brought into play when selecting complex phonetic stimuli for further analysis are similar to those engaged when selecting between tones of different frequencies, in contrast with previous theoretical interpretations. Latency differences observed between the attention-related ERPs to simple and complex stimuli were attributed to differences in the duration of processing that makes these stimulus features available to attention mechanisms.

Evaluation of an EEG workload model in an Aegis simulation environment

The integration of real-time electroencephalogram (EEG) workload indices into the man-machine interface could greatly enhance performance of complex tasks, transforming traditionally passive human-system interaction (HSI) into an active exchange where physiological indicators adjust the interaction to suit a user’s engagement level. The envisioned outcome is a closed-loop system that utilizes EEG and other physiological indices for dynamic regulation and optimization of HSI in real-time. As a first step towards a closed-loop system, five individuals performed as identification supervisors (IDSs) in an Aegis command and control (C2) simulated environment, a combat system with advanced, automatic detect-and-track, multi-function phased array radar. The Aegis task involved monitoring multiple data sources (i.e., missile-tracks, alerts, queries, resources), detecting required actions, responding appropriately, and ensuring system status remains within desired parameters. During task operation, a preliminary workload measure calculated in real-time for each second of EEG and was used to manipulate the Aegis task demands. In post-hoc analysis, the use of a five-level workload measure to detect cognitively challenging events was evaluated. Events in decreasing order of difficulty were track selection-identification, alert-responses, hooking-tracks, and queries. High/extreme EEG-workload occurred during high cognitive-load tasks with a detection efficiency approaching 100% for selection-identification and alert-responses, 77% for hooking-tracks and 70% for queries. Over 95% of high/extreme EEG-workload across participants occurred during high-difficulty events (false positive rate < 5%). The high/extreme workload occurred between 25-30% of time. These results suggest an intelligent closed-loop system incorporating EEG-workload measures could be designed to re-allocate tasks and aid in efficiently streamlining a user’s cognitive workload. Such an approach could ensure the operator remains uninterrupted during high/extreme workload periods, thereby resulting in increased productivity and reduced errors.

Cognitive Neurophysiologic Synchronies: What Can They Contribute to the Study of Teamwork?

Objective: Cognitive neurophysiologic synchronies (NS) are low-level data streams derived from electroencephalography (EEG) measurements that can be collected and analyzed in near real time and in realistic settings. The objective of this study was to relate the expression of NS for engagement to the frequency of conversation between team members during Submarine Piloting and Navigation (SPAN) simulations. Background: If the expression of different NS patterns is sensitive to changes in the behavior of teams, they may be a useful tool for studying team cognition. Method: EEG-derived measures of engagement (EEG-E) 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 was mapped onto team events and related to the frequency of team members’ speech. Standardized models were created with pooled data from multiple teams to facilitate comparisons across teams and levels of expertise and to provide a framework for rapid monitoring of team performance. Results: The NS expression for engagement shifted across task segments and internal and external task changes. These changes occurred within seconds and were affected more by changes in the task than by the person speaking. Shannon entropy measures of the NS data stream showed decreases associated with periods when the team was stressed and speaker entropy was high. Conclusion: These studies indicate that expression of neurophysiologic indicators measured by EEG may complement rather than duplicate communication metrics as measures of team cognition. Application: Neurophysiologic approaches may facilitate the rapid determination of the cognitive status of a team and support the development of novel adaptive approaches to optimize team function.

Modeling the neurodynamic complexity of submarine navigation teams

Our objective was to apply ideas from complexity theory to derive neurophysiologic models of Submarine Piloting and Navigation showing how teams cognitively organize around changes in the task and how this organization is altered with experience. The cognitive metric highlighted was an electroencephalography (EEG)-derived measure of engagement (termed NS_E) which was modeled into a collective team variable showing the engagement of each of 6 team members as well as the engagement of the team as a whole. We show that during a navigation task the NS_E data stream contains historical information about the cognitive organization of the team and that this organization can be quantified by fluctuations in the Shannon entropy of the data stream.The fluctuations in the NS_E entropy were complex, showing both rapid changes over a period of seconds and longer fluctuations that occurred over periods of minutes. The periods of low NS_E entropy represented moments when the team’s cognition had undergone significant re-organization, i.e. when fewer NS_E symbols were being expressed.Decreases in NS_E entropy were associated with periods of poorer team performance as indicated by delays/omissions in the regular determination of the submarine’s position; parallel communication data suggested that these were also periods of increased stress.Experienced submarine navigation teams performed better than Junior Officer teams, had higher overall levels of NS_E entropy and appeared more cognitively flexible as indicated by the use of a larger repertoire of available NS_E patterns.The quantitative information in the NS_E entropy may provide a framework for designing future adaptive team training systems as it can be modeled and reported in near real time.

EEG-based classification of positive and negative affective states

This study aimed to identify the neurophysiological correlates of two primary aroused affective states related to positive and negative emotions, and to create a classification model for each second of data. General and individualized models were built on the EEG data recorded from 98 participants while watching two contrasting ~20 min videos – one to elicit a negative affective state, and the other to induce positive affect. The final models were cross-validated on an additional 63 participants and the classifiers achieved similar results. The classifiers’ generalization capability was further estimated in a related study where 63 participants returned to watch videos that incorporated narratives with varying levels of fairness, justice, and character identification.

EEG quantification of alertness: Methods for early identification of individuals most susceptible to sleep deprivation

Electroencephalographic (EEG) and neurocognitive measures were simultaneously acquired to quantify alertness from 24 participants during 44-hours of sleep deprivation. Performance on a three-choice vigilance task (3C-VT), paired-associate learning/memory task (PAL) and modified Maintenance of Wakefulness Test (MWT), and sleep technician-observed drowsiness (eye-closures, head-nods, EEG slowing) were quantified. The B-Alert system automatically classifies each second of EEG on an alertness/drowsiness continuum. B-Alert classifications were significantly correlated with technician-observations, visually scored EEG and performance measures. B-Alert classifications during 3C-VT, and technician observations and performance during the 3C-VT and PAL evidenced progressively increasing drowsiness as a result of sleep deprivation with a stabilizing effect observed at the batteries occurring between 0600 and 1100 suggesting a possible circadian effect similar to those reported in previous sleep deprivation studies. Participants were given an opportunity to take a 40-minute nap approximately 24-hours into the sleep deprivation portion of the study (i.e., 7 PM on Saturday). The nap was followed by a transient period of increased alertness. Approximately 8 hours after the nap, behavioral and physiological measures of drowsiness returned to levels prior to the nap. Cluster analysis was used to stratify individuals into three groups based on their level of impairment as a result of sleep deprivation. The combination of B-Alert and neuro-behavioral measures may identify individuals whose performance is most susceptible to sleep deprivation. These objective measures could be applied in an operational setting to provide a “biobehavioral assay” to determine vulnerability to sleep deprivation.

Construction and validation of a neurophysio-technological framework for imagery analysis

Intelligence analysts are bombarded with enormous volumes of imagery, which they must visually filter through to identify relevant areas of interest. Interpretation of such data is subject to error due to (1) large data volumes, implying the need for faster and more effective processing, and (2) misinterpretation, implying the need for enhanced analyst/system effectiveness. This paper outlines the Revolutionary Accelerated Processing Image Detection (RAPID) System, designed to significantly improve data throughput and interpretation by incorporating advancing neurophysiological technology to monitor processes associated with detection and identification of relevant target stimuli in a non-invasive and temporally precise manner. Specifically, this work includes the development of innovative electroencephalographic (EEG) and eye tracking technologies to detect and flag areas of interest, potentially without an analysts conscious intervention or motor responses, while detecting and mitigating problems with tacit knowledge, such as anchoring bias in real-time to reduce the possibility of human error.

Retrospective cross-validation of automated sleep staging using electroocular recording in patients with and without sleep disordered breathing

Background Alterations of sleep duration and architecture have been associated with increased morbidity and mortality, and specifically linked to chronic cardiovascular disease and psychiatric disorders, such as type 2 diabetes or depression. Measurement of sleep quality to assist in the diagnosis or treatment of these diseases is not routinely performed due to the complexity and cost of conventional methods. The objective of this study is to cross-validate the accuracy of an automated algorithm that stages sleep from the EEG signal acquired with sensors that can be self-applied by patients. Methods This retrospective study design included polymsomnographic records from 19 presumably healthy individuals and 68 patients suspected of having sleep disordered breathing (SDB). Epoch-by-epoch comparisons were made between manual vs. automated sleeps staging (from the left and right electrooculogram) with the impact of SDB severity considered. Results Both scoring methods reported decreased Stage N3 and REM and increased wake and N1 as SDB severity increased. Inter-class correlations and Kappa coefficients were strong across all stages except N1. Agreements across all epochs for subjects with normal and patients with mild SDB were: wake = 80%, N1 = 25%, N2 = 78%, N3 = 84% and REM = 75%. Agreement decreased in patients with moderate and severe SDB amounting to: wake = 71%, N1 = 30%, N2 = 71%, N3 = 65%, and REM = 67%. Differences in detection of sleep onset were within three-minutes in 48 % of the subjects and 10-min in 73 % of the cases and were not impacted by SDB severity. Automated staging slightly underestimated total sleep time but this difference had a limited impact on the respiratory disturbance indexes. Conclusions This cross-validation study demonstrated that measurement of sleep architecture obtained from a single-channel of forehead EEG can be equivalent to between-rater agreement using conventional manual scoring. The accuracies obtained with automated sleep staging were inversely proportional to SDB severity at a rate similar to manual scorers. These results suggest that the automated sleep staging used in this study may prove useful in evaluating sleep quality in patients with chronic diseases.

Identifying psychophysiological indices of expert vs. novice performance in deadly force judgment and decision making

Objective: To demonstrate that psychophysiology may have applications for objective assessment of expertise development in deadly force judgment and decision making (DFJDM). Background: Modern training techniques focus on improving decision-making skills with participative assessment between trainees and subject matter experts primarily through subjective observation. Objective metrics need to be developed. The current proof of concept study explored the potential for psychophysiological metrics in deadly force judgment contexts. Method: Twenty-four participants (novice, expert) were recruited. All wore a wireless Electroencephalography (EEG) device to collect psychophysiological data during high-fidelity simulated deadly force judgment and decision-making simulations using a modified Glock firearm. Participants were exposed to 27 video scenarios, one-third of which would have justified use of deadly force. Pass/fail was determined by whether the participant used deadly force appropriately. Results: Experts had a significantly higher pass rate compared to novices (p < 0.05). Multiple metrics were shown to distinguish novices from experts. Hierarchical regression analyses indicate that psychophysiological variables are able to explain 72% of the variability in expert performance, but only 37% in novices. Discriminant function analysis (DFA) using psychophysiological metrics was able to discern between experts and novices with 72.6% accuracy. Conclusion: While limited due to small sample size, the results suggest that psychophysiology may be developed for use as an objective measure of expertise in DFDJM. Specifically, discriminant function measures may have the potential to objectively identify expert skill acquisition. Application: Psychophysiological metrics may create a performance model with the potential to optimize simulator-based DFJDM training. These performance models could be used for trainee feedback, and/or by the instructor to assess performance objectively.

Neurotechnology to Accelerate Learning: During Marksmanship Training

This article explores the psychophysiological metrics during expert and novice performances in marksmanship, combat deadly force judgment and decision making (DFJDM), and interactions of teams. Electroencephalography (EEG) and electrocardiography (ECG) are used to characterize the psychophysiological profiles within all categories. Closed-loop biofeedback was administered to accelerate learning during marksmanship training in which the results show a difference in groups that received feedback compared with the control. During known distance marksmanship and DFJDM scenarios, experts show superior ability to control physiology to meet the demands of the task. Expertise in teaming scenarios is characterized by higher levels of cohesiveness than those seen in novices.