Complexity synchronization in emergent intelligence.

In this work, we use a simple multi-agent-based-model (MABM) of a social network, implementing selfish algorithm (SA) agents, to create an adaptive environment and show, using a modified diffusion entropy analysis (DEA), that the mutual-adaptive interaction between the parts of such a network manifests complexity synchronization (CS). CS has been shown to exist by processing simultaneously measured time series from among organ-networks (ONs) of the brain (neurophysiology), lungs (respiration), and heart (cardiovascular reactivity) and to be explained theoretically as a synchronization of the multifractal dimension (MFD) scaling parameters characterizing each time series. Herein, we find the same kind of CS in the emergent intelligence of groups formed in a self-organized social interaction without macroscopic control but with biased self-interest between two groups of agents playing an anti-coordination game. This computational result strongly suggests the existence of the same CS in real-world social phenomena and in human-machine interactions as that found empirically in ONs.

Complexity Synchronization of Organ Networks.

The transdisciplinary nature of science as a whole became evident as the necessity for the complex nature of phenomena to explain social and life science, along with the physical sciences, blossomed into complexity theory and most recently into complexitysynchronization. This science motif is based on the scaling arising from the 1/f-variability in complex dynamic networks and the need for a network of networks to exchange information internally during intra-network dynamics and externally during inter-network dynamics. The measure of complexity adopted herein is the multifractal dimension of the crucial event time series generated by an organ network, and the difference in the multifractal dimensions of two organ networks quantifies the relative complexity between interacting complex networks. Information flows from dynamic networks at a higher level of complexity to those at lower levels of complexity, as summarized in the 'complexity matching effect', and the flow is maximally efficient when the complexities are equal. Herein, we use the scaling of empirical datasets from the brain, cardiovascular and respiratory networks to support the hypothesis that complexity synchronization occurs between scaling indices or equivalently with the matching of the time dependencies of the networks' multifractal dimensions.

Complexity synchronization: a measure of interaction between the brain, heart and lungs.

Herein we address the measurable consequences of the network effect (NE) on time series generated by different parts of the brain, heart, and lung organ-networks (ONs), which are directly related to their inter-network and intra-network interactions. Moreover, these same physiologic ONs have been shown to generate crucial event (CE) time series, and herein are shown, using modified diffusion entropy analysis (MDEA) to have scaling indices with quasiperiodic changes in complexity, as measured by scaling indices, over time. Such time series are generated by different parts of the brain, heart, and lung ONs, and the results do not depend on the underlying coherence properties of the associated time series but demonstrate a generalized synchronization of complexity. This high-order synchrony among the scaling indices of EEG (brain), ECG (heart), and respiratory time series is governed by the quantitative interdependence of the multifractal behavior of the various physiological ONs' dynamics. This consequence of the NE opens the door for an entirely general characterization of the dynamics of complex networks in terms of complexity synchronization (CS) independently of the scientific, engineering, or technological context. CS is truly a transdisciplinary effect.

Anticipatory cardiac deceleration estimates cognitive performance in virtual reality beyond tonic heart period and heart period variability.

Anticipatory cardiac deceleration is the lengthening of heart period before an expected event. It appears to reflect preparation that supports rapid action. The current study sought to bolster anticipatory deceleration as a practical and unique estimator of performance efficiency. To this end, we examined relationships between deceleration and virtual reality performance under low and high time pressure. Importantly, we investigated whether deceleration separately estimates performance beyond basal heart period and basal high-frequency heart rate variability (other vagally influenced metrics related to cognition). Thirty participants completed an immersive virtual reality (VR) cognitive performance task across six longitudinal sessions. Anticipatory deceleration and basal heart period/heart period variability were quantified from electrocardiography collected during pre-task anticipatory countdowns and baseline periods, respectively. At the between-person level, we found that greater anticipatory declaration was related to superior accuracy and faster response times (RT). The relation between deceleration and accuracy was stronger under high relative to low time pressure, when good performance requires greater efficiency. Findings for heart period and heart period variability largely converge with the prior literature, but importantly, were statistically separate from deceleration effects on performance. Lastly, deceleration effects were detected using anticipatory periods that are more practical (shorter and more intermittent) than those typically employed. Taken together, findings suggest that anticipatory deceleration is a unique and practical correlate of cognitive-motor efficiency apart from heart period and heart period variability in virtual reality.

Temporal complexity measure of reaction time series: Operational versus event time.

INTRODUCTION: Detrended fluctuation analysis (DFA) is a well-established method to evaluate scaling indices of time series, which categorize the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time Y(n) time series, where n is the trial number. METHODS: Herein we propose treating each reaction time as a duration time that changes the representation from operational (trial number) time n to event (temporal) time t, or X(t). The DFA algorithm was then applied to the X(t) time series to evaluate scaling indices. The dataset analyzed is based on a Go-NoGo shooting task that was performed by 30 participants under low and high time-stress conditions in each of six repeated sessions over a 3-week period. RESULTS: This new perspective leads to quantitatively better results in (1) differentiating scaling indices between low versus high time-stress conditions and (2) predicting task performance outcomes. CONCLUSION: We show that by changing from operational time to event time, the DFA allows discrimination of time-stress conditions and predicts performance outcomes.

Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study.

Previous neurofeedback research has shown training-related frontal theta increases and performance improvements on some executive tasks in real feedback versus sham control groups. However, typical sham control groups receive false or non-contingent feedback, making it difficult to know whether observed differences between groups are associated with accurate contingent feedback or other cognitive mechanisms (motivation, control strategies, attentional engagement, fatigue, etc.). To address this question, we investigated differences between two frontal theta training groups, each receiving accurate contingent feedback, but with different top-down goals: (1) increase and (2) alternate increase/decrease. We hypothesized that the increase group would exhibit greater increases in frontal theta compared to the alternate group, which would exhibit lower frontal theta during down- versus up-modulation blocks over sessions. We also hypothesized that the alternate group would exhibit greater performance improvements on a Go-NoGo shooting task requiring alterations in behavioral activation and inhibition, as the alternate group would be trained with greater task specificity, suggesting that receiving accurate contingent feedback may be the more salient learning mechanism underlying frontal theta neurofeedback training gains. Thirty young healthy volunteers were randomly assigned to increase or alternate groups. Training consisted of an orientation session, five neurofeedback training sessions (six blocks of six 30-s trials of FCz theta modulation (4-7 Hz) separated by 10-s rest intervals), and six Go-NoGo testing sessions (four blocks of 90 trials in both Low and High time-stress conditions). Multilevel modeling revealed greater frontal theta increases in the alternate group over training sessions. Further, Go-NoGo task performance increased at a greater rate in the increase group (accuracy and reaction time, but not commission errors). Overall, these results reject our hypotheses and suggest that changes in frontal theta and performance outcomes were not explained by reinforcement learning afforded by accurate contingent feedback. We discuss our findings in terms of alternative conceptual and methodological considerations, as well as limitations of this research.

Overlapping brain network and alpha power changes suggest visuospatial attention effects on driving performance.

When humans perform prolonged, continuous tasks, their performance fluctuates. The etiology of these fluctuations is multifactorial, but they are influenced by changes in attention reflected in underlying neural dynamics. Previous work with electroencephalography has suggested that prestimulus alpha power is a neural signature of attention allocation with higher power portending relatively poorer performance. The functional mechanisms subserving these changes in alpha power and behavior are postulated to be the result of networked neural activity that permits flexibility in the allocation of attention. Here, we directly examine the similarity between prestimulus alpha connectivity and power in relation to performance fluctuations in a continuous driving task. Participants were asked to maintain their vehicle in the center of a simulated highway, and we evaluated their performance by randomly perturbing the vehicle and assessing their steering correction. We then used the 3 seconds of neural activity before the unexpected event to derive alpha functional connectivity in the first analysis and alpha power in the second analysis, and we employed linear regression to separately investigate their relationship to 3 metrics of driving performance (lane deviation, reaction time (RT), and heading error). We find that the locations involved in our network analysis also show the strongest modulation of alpha activity. Interestingly, the network pattern suggests a posterior to anterior directionality, consistent with bottom-up theories of attention, and these results may reflect a gain control model of attention in which ongoing attention is modulated through coordinated, network activity. (PsycINFO Database Record

Independent Component Analysis and Source Localization on Mobile EEG Data Can Identify Increased Levels of Acute Stress.

Mobile electroencephalography (EEG) is a very useful tool to investigate the physiological basis of cognition under real-world conditions. However, as we move experimentation into less-constrained environments, the influence of state changes increases. The influence of stress on cortical activity and cognition is an important example. Monitoring of modulation of cortical activity by EEG measurements is a promising tool for assessing acute stress. In this study, we test this hypothesis and combine EEG with independent component analysis and source localization to identify cortical differences between a control condition and a stressful condition. Subjects performed a stationary shooting task using an airsoft rifle with and without the threat of an experimenter firing a different airsoft rifle in their direction. We observed significantly higher skin conductance responses and salivary cortisol levels (p < 0.05 for both) during the stressful conditions, indicating that we had successfully induced an adequate level of acute stress. We located independent components in five regions throughout the cortex, most notably in the dorsolateral prefrontal cortex, a region previously shown to be affected by increased levels of stress. This area showed a significant decrease in spectral power in the theta and alpha bands less than a second after the subjects pulled the trigger. Overall, our results suggest that EEG with independent component analysis and source localization has the potential of monitoring acute stress in real-world environments.

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.

Common EEG features for behavioral estimation in disparate, real-world tasks.

In this study we explored the potential for capturing the behavioral dynamics observed in real-world tasks from concurrent measures of EEG. In doing so, we sought to develop models of behavior that would enable the identification of common cross-participant and cross-task EEG features. To accomplish this we had participants perform both simulated driving and guard duty tasks while we recorded their EEG. For each participant we developed models to estimate their behavioral performance during both tasks. Sequential forward floating selection was used to identify the montage of independent components for each model. Linear regression was then used on the combined power spectra from these independent components to generate a continuous estimate of behavior. Our results show that oscillatory processes, evidenced in EEG, can be used to successfully capture slow fluctuations in behavior in complex, multi-faceted tasks. The average correlation coefficients between the actual and estimated behavior was 0.548 ± 0.117 and 0.701 ± 0.154 for the driving and guard duty tasks respectively. Interestingly, through a simple clustering approach we were able to identify a number of common components, both neural and eye-movement related, across participants and tasks. We used these component clusters to quantify the relative influence of common versus participant-specific features in the models of behavior. These findings illustrate the potential for estimating complex behavioral dynamics from concurrent measures from EEG using a finite library of universal features.

Differential Functionality of Right and Left Parietal Activity in Controlling a Motor Vehicle.

Driving a motor vehicle is an inherently complex task that requires robust control to avoid catastrophic accidents. Drivers must maintain their vehicle in the middle of the travel lane to avoid high speed collisions with other traffic. Interestingly, while a vehicle's lane deviation (LD) is critical, studies have demonstrated that heading error (HE) is one of the primary variables drivers use to determine a steering response, which directly controls the position of the vehicle in the lane. In this study, we examined how the brain represents the dichotomy between control/response parameters (heading, reaction time (RT), and steering wheel corrections) and task-critical parameters (LD). Specifically, we examined electroencephalography (EEG) alpha band power (8-13 Hz) from estimated sources in right and left parietal regions, and related this activity to four metrics of driving performance. Our results demonstrate differential task involvement between the two hemispheres: right parietal activity was most closely related to LD, whereas left parietal activity was most closely related to HE, RT and steering responses. Furthermore, HE, RT and steering wheel corrections increased over the duration of the experiment while LD did not. Collectively, our results suggest that the brain uses differential monitoring and control strategies in the right and left parietal regions to control a motor vehicle. Our results suggest that the regulation of this control changes over time while maintaining critical task performance. These results are interpreted in two complementary theoretical frameworks: the uncontrolled manifold and compensatory control theories. The central tenet of these frameworks permits performance variability in parameters (i.e., HE, RT and steering) so far as it does not interfere with critical task execution (i.e., LD). Our results extend the existing research by demonstrating potential neural substrates for this phenomenon which may serve as potential targets for brain-computer interfaces that predict poor driving performance.

Novel measure of driver and vehicle interaction demonstrates transient changes related to alerting.

Driver behavior and vehicle-road kinematics have been shown to change over prolonged periods of driving; however, the interaction between these two indices has not been examined. Here we develop a measure that examines how drivers turn the steering wheel relative to heading error velocity, which the authors call the relative steering wheel compensation (RSWC). The RSWC transiently changes on a short time scale coincident with a verbal query embedded within the study paradigm. In contrast, more traditional variables are dynamic over longer time scales consistent with previous research. The results suggest drivers alter their behavioral output (steering wheel correction) relative to sensory input (vehicle heading error velocity) on a distinct temporal scale and may reflect an interaction of alerting and control.

Evolution of cerebral cortico-cortical communication during visuomotor adaptation to a cognitive-motor executive challenge.

Cortical dynamics were examined during a cognitive-motor adaptation task that required inhibition of a familiar motor plan. EEG coherence between the motor planning (Fz) and left hemispheric region was progressively reduced over trials (low-beta, high-beta, gamma bands) along with faster, straighter reaching movements during both planning and execution. The major reduction in coherence (delta, low/high-theta, low/high-alpha bands) between Fz and the left prefrontal region during both movement planning and execution suggests gradual disengagement of frontal executive following its initial role in the suppression of established visuomotor maps. Also, change in the directionality of phase lags (delta, high-alpha, high-beta, gamma bands) reflects a progressive shift from feedback to feedforward motor control. The reduction of cortico-cortical communication, particularly in the frontal region, and the strategic feedback/feedforward mode shift translated as higher quality motor performance. This study extends our understanding of the role of frontal executive beyond purely cognitive tasks to cognitive-motor tasks.

Estimating endogenous changes in task performance from EEG.

Brain wave activity is known to correlate with decrements in behavioral performance as individuals enter states of fatigue, boredom, or low alertness.Many BCI technologies are adversely affected by these changes in user state, limiting their application and constraining their use to relatively short temporal epochs where behavioral performance is likely to be stable. Incorporating a passive BCI that detects when the user is performing poorly at a primary task, and adapts accordingly may prove to increase overall user performance. Here, we explore the potential for extending an established method to generate continuous estimates of behavioral performance from ongoing neural activity; evaluating the extended method by applying it to the original task domain, simulated driving; and generalizing the method by applying it to a BCI-relevant perceptual discrimination task. Specifically, we used EEG log power spectra and sequential forward floating selection (SFFS) to estimate endogenous changes in behavior in both a simulated driving task and a perceptual discrimination task. For the driving task the average correlation coefficient between the actual and estimated lane deviation was 0.37 ± 0.22 (µ ± σ). For the perceptual discrimination task we generated estimates of accuracy, reaction time, and button press duration for each participant. The correlation coefficients between the actual and estimated behavior were similar for these three metrics (accuracy = 0.25 ± 0.37, reaction time = 0.33 ± 0.23, button press duration = 0.36 ± 0.30). These findings illustrate the potential for modeling time-on-task decrements in performance from concurrent measures of neural activity.

Usability of four commercially-oriented EEG systems.

Electroencephalography (EEG) holds promise as a neuroimaging technology that can be used to understand how the human brain functions in real-world, operational settings while individuals move freely in perceptually-rich environments. In recent years, several EEG systems have been developed that aim to increase the usability of the neuroimaging technology in real-world settings. Here, the usability of three wireless EEG systems from different companies are compared to a conventional wired EEG system, BioSemi's ActiveTwo, which serves as an established laboratory-grade 'gold standard' baseline. The wireless systems compared include Advanced Brain Monitoring's B-Alert X10, Emotiv Systems' EPOC and the 2009 version of QUASAR's Dry Sensor Interface 10-20. The design of each wireless system is discussed in relation to its impact on the system's usability as a potential real-world neuroimaging system. Evaluations are based on having participants complete a series of cognitive tasks while wearing each of the EEG acquisition systems. This report focuses on the system design, usability factors and participant comfort issues that arise during the experimental sessions. In particular, the EEG systems are assessed on five design elements: adaptability of the system for differing head sizes, subject comfort and preference, variance in scalp locations for the recording electrodes, stability of the electrical connection between the scalp and electrode, and timing integration between the EEG system, the stimulus presentation computer and other external events.

Mental stress and trapezius muscle activation under psychomotor challenge: a focus on EMG gaps during computer work.

Momentary reductions in the electrical activity of working muscles (EMG gaps) contribute to the explanation for the relationship between psychosocial stress and musculoskeletal problems in computer work. EMG activity and gaps in the left and right trapezii were monitored in 23 participants under low and high mental workload (LMW and HMW) demands during computer data entry. Increases in EMG activity and decreases in EMG-gap frequencies in both left and right trapezius muscles were greater during HMW than LMW. In addition, heart period and end-tidal CO2 were lower during HMW, whereas self-reported mood states were higher during HMW. The correspondence between lower end-tidal CO2 and lower EMG-gap frequencies suggests that hyperventilation (overbreathing) may mediate trapezius muscle activation. The reduction of EMG gaps suggests that the salutary benefits of momentary rest from musculoskeletal work are diminished during mental stress.

Event-related cortical dynamics of soldiers during shooting as a function of varied task demand.

INTRODUCTION: Cortical dynamics of soldiers were examined during a reactive shooting task under varied task demands to investigate the effects of cognitive load on functional states of soldiers in real-time. METHODS: Task demand factors consisted of task load (single, dual), decision load (no-decision, choice-decision), and target-exposure time (short, long). The Dismounted Infantryman Survivability and Lethality Testbed (DISALT) shooting simulator was programmed to generate the eight shooting scenarios and record weapon aim-point data while EEG was acquired continuously during each scenario. Event-related spectral perturbation (ERSP) was derived from single-trial data time-locked to the onsets of targets and peak amplitude and latency measures were analyzed in theta (4-7 Hz) and upper alpha (11-13 Hz) frequency bands. RESULTS: The results are as follows: 1) a stimulus-evoked ERSP theta peak exhibited higher amplitude in the parietal region for choice- vs. no-decision scenarios and longer latency in the right central and temporal regions for dual- vs. single-task scenarios; and 2) ERSP alpha exhibited a progressive increase following the onset of targets with less of an increase in the left central region for dual- vs. single-task scenarios. DISCUSSION: ERSP theta synchronization reflects stimulus encoding and exhibits increased peak power with more complex decision demands and longer latency with secondary task demands, whereas ERSP alpha synchronization reflects motor preparation and exhibits less of an increase with secondary task demands during reactive target shooting tasks. This research contributes the first study of cortical dynamics of soldiers performing a reactive shooting task and has implications for theories of attention and cognitive workload, human factors engineering, and soldier performance.

Independent component analysis of dynamic brain responses during visuomotor adaptation.

To investigate the spatial and temporal changes in electro-cortical brain activity and hand kinematics during the acquisition of an internal model of a novel screen-cursor transformation, we employed single-trial infomax independent component analysis (ICA), spectral estimation, and kinematics methods. Participants performed center-out drawing movements under normal and rotated visual feedback of pen movements displayed on a computer screen. Clustering of task-related and adaptation-related independent components identified a selective recruitment of brain activation/deactivation foci associated with the exposure to the distorted visual feedback, including networks associated with frontal-, central-, and lateral-posterior alpha rhythms, and frontal-central error-related negativity potential associated with transient theta and low beta rhythms locked to movement onset. Moreover, adaptation to the rotated reference frame was associated with a reduction in the imposed directional bias and decreases in movement path length and movement time by late-exposure trials, as well as after-effects after removal of the visual distortion. The underlying spatiotemporal pattern of activations is consistent with recruitment of frontal-parietal, sensory-motor, and anterior cingulate cortical areas during visuomotor adaptation.

Cerebral cortical adaptations associated with visuomotor practice.

PURPOSE: Electroencephalographic (EEG) recordings were examined at the temporal (T3, T4) regions of the cerebral cortex in novice pistol shooters (N = 11) over a training period of 12-14 wk to determine changes in activation. Mean alpha power and its rate of change were hypothesized to increase in the left temporal region during aiming from early to late season as participants improved their accuracy and reduced cognitive effort. METHODS: Event-related alpha II power (ERAP; 11-13 Hz) was examined over a 5-s period preceding the trigger pull during shooting (SH) and two control conditions (resting baseline, BL; and postural simulation, PS) at early (time 1), middle (time 2), and late (time 3) practice. RESULTS: Mean levels of ERAP increased at T3 from the beginning to the end of the training period during both SH and PS, but not BL, whereas no such change in mean level of ERAP was noted at T4 during any of the three conditions. The practice-related cortical adaptation during SH covaried with an increase in shooting percentage over the season. A higher rate of increase in ERAP during the 5-s aiming period of SH relative to that at PS and BL was also observed throughout training at both T3 and T4. Exploratory analysis of global power (sites F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4) revealed that ERAP increased during SH from time 1 to time 3 at all sites except Fz and Pz, whereas only one site (C4) revealed an increase during BL. CONCLUSIONS: The reduction in cortical activity is likely due to sensorimotor integration and less cognitive effort due to automaticity.