Evaluating Operator Training Performance Using Recurrence Quantification Analysis of Autocorrelation Transformed Eye Gaze Data.

OBJECTIVE: This research aimed to investigate the relationship between gaze behaviour dynamics and operator performance. BACKGROUND: Individuals differ in their approach when learning a new task often resulting in performance disparity. During training some individuals learn the structure and dynamics of the task and develop a systematic approach, whereas others may achieve the same result albeit with increased perceived workload, or indeed some may fail to achieve superior performance levels. Previous research has shown that comparing gaze of experts with novices can provide unique insights into cognitive functioning of superior performers. METHODS: Twenty-five individuals participated in a computer-based simulation task. The concept of coefficient of variation (CoV) of task scores was used to compute the participants' consistency of performance. Based on CoV, the cohort was split into two performance categories. The temporal patterns in participants gaze data were transformed using autocorrelation, and recurrence quantification analysis (RQA) was employed to analyse and quantify the patterns. RESULTS: A Mann-Whitney U analysis demonstrated significantly (p < .01) higher determinism, entropy and laminarity in the superior group compared to the moderate group. Pearson's correlation revealed a significant (p < .01) negative correlation between the consistency of task performance (CoV) and the RQA measures. CONCLUSION: The results demonstrated that eye gaze dynamics can be used as an objective measure of performance. Participants classified as superior performers consistently demonstrated a systematic gaze activity which were in line with the task structure. APPLICATION: The methods presented here are applicable to observe and evaluate operators' strategic distribution of gaze. Specifically, for tactical monitoring and decision making in task environments where spatial locations of elements-of-interest vary continuously.

Effects of Dynamic Resilience on the Reactivity of Vagally Mediated Heart Rate Variability.

Dynamic resilience is a novel concept that aims to quantify how individuals are coping while operating in dynamic and complex task environments. A recently developed dynamic resilience measure, derived through autoregressive modeling, offers an avenue toward dynamic resilience classification that may yield valuable information about working personnel for industries such as defense and elite sport. However, this measure classifies dynamic resilience based upon in-task performance rather than self-regulating cognitive structures; thereby, lacking any supported self-regulating cognitive links to the dynamic resilience framework. Vagally mediated heart rate variability (vmHRV) parameters are potential physiological measures that may offer an opportunity to link self-regulating cognitive structures to dynamic resilience given their supported connection to the self-regulation of stress. This study examines if dynamic resilience classifications reveal significant differences in vagal reactivity between higher, moderate and lower dynamic resilience groups, as participants engage in a dynamic, decision-making task. An amended Three Rs paradigm was implemented that examined vagal reactivity across six concurrent vmHRV reactivity segments consisting of lower and higher task load. Overall, the results supported significant differences between higher and moderate dynamic resilience groups' vagal reactivity but rejected significant differences between the lower dynamic resilience group. Additionally, differences in vagal reactivity across vmHRV reactivity segments within an amended Three Rs paradigm were partially supported. Together, these findings offer support toward linking dynamic resilience to temporal self-regulating cognitive structures that play a role in mediating physiological adaptations during task engagement.

Validation of Polar OH1 optical heart rate sensor for moderate and high intensity physical activities.

BACKGROUND: Optical measurement techniques and recent advances in wearable technology have made heart rate (HR) sensing simpler and more affordable. OBJECTIVES: The Polar OH1 is an arm worn optical heart rate monitor. The objectives of this study are two-fold; 1) to validate the OH1 optical HR sensor with the gold standard of HR measurement, electrocardiography (ECG), over a range of moderate to high intensity physical activities, 2) to validate wearing the OH1 at the temple as an alternative location to its recommended wearing location around the forearm and upper arm. METHODS: Twenty-four individuals participated in a physical exercise protocol, by walking on a treadmill and riding a stationary spin bike at different speeds while the criterion measure, ECG and Polar OH1 HR were recorded simultaneously at three different body locations; forearm, upper arm and the temple. Time synchronised HR data points were compared using Bland-Altman analyses and intraclass correlation. RESULTS: The intraclass correlation between the ECG and Polar OH1, for the aggregated data, was 0.99 and the estimated mean bias ranged 0.27-0.33 bpm for the sensor locations. The three sensors exhibited a 95% limit of agreement (LoA: forearm 5.22, -4.68 bpm; upper arm 5.15, -4.49; temple 5.22, -4.66). The mean of the ECG HR for the aggregated data was 112.15 ± 24.52 bpm. The intraclass correlation of HR values below and above this mean were 0.98 and 0.99 respectively. The reported mean bias ranged 0.38-0.47 bpm (95% LoA: forearm 6.14, -5.38 bpm; upper arm 6.07, -5.13 bpm; temple 6.09, -5.31 bpm), and 0.15-0.16 bpm (95% LoA: forearm 3.99, -3.69 bpm; upper arm 3.90, -3.58 bpm; temple 4.06, -3.76 bpm) respectively. During different exercise intensities, the intraclass correlation ranged 0.95-0.99 for the three sensor locations. During the entire protocol, the estimated mean bias was in the range -0.15-0.55 bpm, 0.01-0.53 bpm and -0.37-0.48 bpm, for the forearm, upper arm and temple locations respectively. The corresponding upper limits of 95% LoA were 3.22-7.03 bpm, 3.25-6.82 bpm and 3.18-7.04 bpm while the lower limits of 95% LoA were -6.36-(-2.35) bpm, -6.46-(-2.30) bpm and -7.42-(-2.41) bpm. CONCLUSION: Polar OH1 demonstrates high level of agreement with the criterion measure ECG HR, thus can be used as a valid measure of HR in lab and field settings during moderate and high intensity physical activities.

A fresh look at functional link neural network for motor imagery-based brain-computer interface.

BACKGROUND: Artificial neural networks (ANNs) are one of the widely used classifiers in the brain-computer interface (BCI) systems-based on noninvasive electroencephalography (EEG) signals. Among the different ANN architectures, the most commonly applied for BCI classifiers is the multilayer perceptron (MLP). When appropriately designed with optimal number of neuron layers and number of neurons per layer, the ANN can act as a universal approximator. However, due to the low signal-to-noise ratio of EEG signal data, overtraining problem may become an inherent issue, causing these universal approximators to fail in real-time applications. NEW METHOD: In this study we introduce a higher order neural network, namely the functional link neural network (FLNN) as a classifier for motor imagery (MI)-based BCI systems, to remedy the drawbacks in MLP. RESULTS: We compare the proposed method with competing classifiers such as linear decomposition analysis, naïve Bayes, k-nearest neighbours, support vector machine and three MLP architectures. Two multi-class benchmark datasets from the BCI competitions are used. Common spatial pattern algorithm is utilized for feature extraction to build classification models. COMPARISON WITH EXISTING METHOD(S): FLNN reports the highest average Kappa value over multiple subjects for both the BCI competition datasets, under similarly preprocessed data and extracted features. Further, statistical comparison results over multiple subjects show that the proposed FLNN classification method yields the best performance among the competing classifiers. CONCLUSIONS: Findings from this study imply that the proposed method, which has less computational complexity compared to the MLP, can be implemented effectively in practical MI-based BCI systems.

An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network.

Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.

Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method.

Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.

A state space based approach in non-linear hemodynamic response modeling with fMRI data.

In this paper we use the modified and integrated version of the balloon model in the analysis of fMRI data. We propose a new state space model realization for this balloon model and represent it with the standard A,B,C and D matrices widely used in system theory. A second order Padé approximation with equal numerator and denominator degree is used for the time delay approximation in the modeling of the cerebral blood flow. The results obtained through numerical solutions showed that the new state space model realization is in close agreement to the actual modified and integrated version of the balloon model. This new system theoretic formulation is likely to open doors to a novel way of analyzing fMRI data with real time robust estimators. With further development and validation, the new model has the potential to devise a generalized measure to make a significant contribution to improve the diagnosis and treatment of clinical scenarios where the brain functioning get altered. Concepts from system theory can readily be used in the analysis of fMRI data and the subsequent synthesis of filters and estimators.