Assessment of human operator functional state using a novel differential evolution optimization based adaptive fuzzy model

With the development of human–machine systems, there has been a growing concern about the consequences of operator performance breakdown under excessive level of workload, especially in safety-critical situations. Assessment and detection of the operator functional state (OFS) enable us to predict the high operational risks of operator. This paper adopts the psychophysiological signals and task performance measures to evaluate OFS under different levels of mental workload. Four indices extracted from electrocardiogram and electroencephalogram, including heart rate (HR), ratio of the standard deviation to the average of HR segment, task load indices (TLI1 and TLI2), are chosen as the inputs of the proposed model. A technique of differential evolution with ant colony search (DEACS) is developed to optimize the parameters of Adaptive-Network-based Fuzzy Inference System (ANFIS). The optimized ANFIS model is employed to estimate the OFS under a series of process control tasks on a simulated software platform of AUTOmation-enhanced Cabin Air Management System. The results showed that the proposed adaptive fuzzy model based on ANFIS and DEACS algorithm is applicable for the operator functional state assessment.     

An adaptive human–machine control system based on multiple fuzzy predictive models of operator functional state

Under the framework of adaptive Human–Machine (HM) systems, it has been proposed that human operators’ task level should be dynamically adjusted according to his/her functional state. The construction of models that can reliably predict the operator functional state (OFS) becomes critical to accomplish such adjustments. However, most of the existing models that evaluate the current OFS by using operators’ current physiological data are static and are of no real predictive capability. Thus, when they are used in adaptive HM systems, the resultant task allocation between operators and machines would be time-delayed. To overcome this problem, a one-step-ahead predictive model concept for OFS computation is proposed. Meanwhile, multiple fuzzy models are developed by using the Wang–Mendel method. These models are able to increase the accuracy of the OFS breakdown prediction, as well as to reduce the model training time. In addition, an adaptive task allocation strategy is designed to validate the proposed models. The results demonstrate that, compared to the conventional HM systems, a 6.7% OFS increment and a 57.1% OFS breakdown decrement can be obtained in the multiple models based adaptive HM systems. The multiple predictive models and the adaptive task allocation strategy would pave the way for future implementations of real-time adaptive HM systems.     

Operator functional state estimation based on EEG-data-driven fuzzy model

This paper proposed a max–min-entropy-based fuzzy partition method for fuzzy model based estimation of human operator functional state (OFS). The optimal number of fuzzy partitions for each I/O variable of fuzzy model is determined by using the entropy criterion. The fuzzy models were constructed by using Wang–Mendel method. The OFS estimation results showed the practical usefulness of the proposed fuzzy modeling approach.     

Auditory Processing under Cross-Modal Visual Load Investigated with Simultaneous EEG-fMRI

Cognitive task demands in one sensory modality (T1) can have beneficial effects on a secondary task (T2) in a different modality, due to reduced top-down control needed to inhibit the secondary task, as well as crossmodal spread of attention. This contrasts findings of cognitive load compromising a secondary modality’s processing. We manipulated cognitive load within one modality (visual) and studied the consequences of cognitive demands on secondary (auditory) processing. 15 healthy participants underwent a simultaneous EEG-fMRI experiment. Data from 8 participants were obtained outside the scanner for validation purposes. The primary task (T1) was to respond to a visual working memory (WM) task with four conditions, while the secondary task (T2) consisted of an auditory oddball stream, which participants were asked to ignore. The fMRI results revealed fronto-parietal WM network activations in response to T1 task manipulation. This was accompanied by significantly higher reaction times and lower hit rates with increasing task difficulty which confirmed successful manipulation of WM load. Amplitudes of auditory evoked potentials, representing fundamental auditory processing showed a continuous augmentation which demonstrated a systematic relation to cross-modal cognitive load. With increasing WM load, primary auditory cortices were increasingly deactivated while psychophysiological interaction results suggested the emergence of auditory cortices connectivity with visual WM regions. These results suggest differential effects of crossmodal attention on fundamental auditory processing. We suggest a continuous allocation of resources to brain regions processing primary tasks when challenging the central executive under high cognitive load.     

Predictive modeling of human operator cognitive state via sparse and robust support vector machines

The accurate prediction of the temporal variations in human operator cognitive state (HCS) is of great practical importance in many real-world safety-critical situations. However, since the relationship between the HCS and electrophysiological responses of the operator is basically unknown, complicated and uncertain, only data-based modeling method can be employed. This paper is aimed at constructing a data-driven computationally intelligent model, based on multiple psychophysiological and performance measures, to accurately estimate the HCS in the context of a safety-critical human–machine system. The advanced least squares support vector machines (LS-SVM), whose parameters are optimized by grid search and cross-validation techniques, are adopted for the purpose of predictive modeling of the HCS. The sparse and weighted LS-SVM (WLS-SVM) were proposed by Suykens et al. to overcome the deficiency of the standard LS-SVM in lacking sparseness and robustness. This paper adopted those two improved LS-SVM algorithms to model the HCS based solely on a set of physiological and operator performance data. The results showed that the sparse LS-SVM can obtain HCS models with sparseness with almost no loss of modeling accuracy, while the WLS-SVM leads to models which are robust in case of noisy training data. Both intelligent system modeling approaches are shown to be capable of capturing the temporal fluctuation trends of the HCS because of their superior generalization performance.     

Gabor filters as texture discriminator

The present paper presents a model for texture discrimination based on Gabor functions. In this model the Gabor power spectrum of the micropatterns corresponding to different textures is calculated. A function that measures the difference between the spectrum of two micropatterns is introduced and its values are correlated with human performance in preattentive detection tasks. In addition, a two stage algorithm for texture segregation is presented. In the first stage the input image is transformed via Gabor filters into a representation image that allows discrimination between features by means of intensity differences. In the second stage the borders between areas of different textures are found using a Laplacian of Gaussian operator. This algorithm is sensitive to energy differences, rotation and spatial frequency and is insensitive to local translation. The model was tested by means of several simulations and was found to be in good correlation with known psychophysical characteristics as texton based texture segregation and micropattern density sensitivity. However, this simple model fails to predict human performance in discrimination tasks based on differences in the density of terminators. In this case human performance is better than expected.     

Voluntary regulation of the functional state and its influence on the effectiveness of cognitive activity during adolescence

We have analyzed the impact of voluntary relaxation on the functional organization and the effectiveness of the voluntary prestimulus attention in performing cognitive tasks in adolescents at the ages of 12–13 and 13–14 years. The effectiveness of cognitive task performance (audio-verbal short-term memory) was estimated on the basis of the number of correctly remembered words. We have found that both age groups are characterized by reduced capacity for voluntary relaxation and its impact on the effectiveness of cognitive task performance. The analysis of the coherence function of EEG rhythmic components in a situation of voluntary attention focused on the performance of cognitive tasks showed no significant change in the interaction of the prefrontal cortex with other cortical areas during the switch from quiet wakefulness or post-relaxation state to the preparation to perform the task, which is typical of adults and young children. Our findings can be a result of sub-optimal functioning of voluntary regulation mechanisms and organization of activities during adolescence.     

Physical Exercise Enhances Cognitive Flexibility as Well as Astrocytic and Synaptic Markers in the Medial Prefrontal Cortex

Physical exercise enhances a wide range of cognitive functions in humans. Running-induced cognitive enhancement has also been demonstrated in rodents but with a strong emphasis on tasks that require the hippocampus. Additionally, studies designed to identify mechanisms that underlie cognitive enhancement with physical exercise have focused on running-induced changes in neurons with little attention paid to such changes in astrocytes. To further our understanding of how the brain changes with physical exercise, we investigated whether running alters performance on cognitive tasks that require the prefrontal cortex and whether any such changes are associated with astrocytic, as well as neuronal, plasticity. We found that running enhances performance on cognitive tasks known to rely on the prefrontal cortex. By contrast, we found no such improvement on a cognitive task known to rely on the perirhinal cortex. Moreover, we found that running enhances synaptic, dendritic and astrocytic measures in several brain regions involved in cognition but that changes in the latter measures were more specific to brain regions associated with cognitive improvements. These findings suggest that physical exercise induces widespread plasticity in both neuronal and nonneuronal elements and that both types of changes may be involved in running-induced cognitive enhancement.     

Application of compression-based distance measures to protein sequence classification: a methodological study

MOTIVATION: Distance measures built on the notion of text compression have been used for the comparison and classification of entire genomes and mitochondrial genomes. The present study was undertaken in order to explore their utility in the classification of protein sequences. RESULTS: We constructed compression-based distance measures (CBMs) using the Lempel-Zlv and the PPMZ compression algorithms and compared their performance with that of the Smith-Waterman algorithm and BLAST, using nearest neighbour or support vector machine classification schemes. The datasets included a subset of the SCOP protein structure database to test distant protein similarities, a 3-phosphoglycerate-kinase sequences selected from archaean, bacterial and eukaryotic species as well as low and high-complexity sequence segments of the human proteome, CBMs values show a dependence on the length and the complexity of the sequences compared. In classification tasks CBMs performed especially well on distantly related proteins where the performance of a combined measure, constructed from a CBM and a BLAST score, approached or even slightly exceeded that of the Smith-Waterman algorithm and two hidden Markov model-based algorithms.     

Suppressing the Encoding of New Information in Memory: A Behavioral Study Derived from Principles of Hippocampal Function

Cognitive processes do not occur in isolation. Interactions between cognitive processes can be observed as a cost in performance following a switch between tasks, a cost that is greatest when the cognitive requirements of the sequential tasks compete. Interestingly, the long-term mnemonic goals associated with specific cognitive tasks can also directly compete. For example, encoding the sequential order in which stimuli are presented in the commonly-utilised 2-Back working memory (WM) tasks is counter-productive to task performance, as this task requires the continual updating of the contents of one''s current mental set. Performance of this task consistently results in reduced activity within the medial temporal lobe (MTL), and this response is believed to reflect the inhibitory mnemonic component of the task. Conversely, there are numerous cognitive paradigms in which participants are explicitly instructed to encode incoming information and performance of these tasks reliably increases MTL activity. Here, we explore the behavioural cost of sequentially performing two tasks with conflicting long-term mnemonic goals and contrasting neural profiles within the MTL. We hypothesised that performing the 2-Back WM prior to a hippocampal-dependent memory task would impair performance on the latter task. We found that participants who performed the 2-Back WM task, prior to the encoding of novel verbal/face-name stimuli, recollected significantly fewer of these stimuli, compared to those who had performed a 0-Back control task. Memory processes believed to be independent of the MTL were unaffected. Our results suggest that the inhibition of MTL-dependent mnemonic function persists beyond the cessation of the 2-Back WM task and can alter performance on entirely separate and subsequently performed memory tasks. Furthermore, they indicate that performance of such tasks may induce a temporarily-sustained, virtual lesion of the hippocampus, which could be used as a probe to explore cognitive processes in the absence of hippocampal involvement.     

Monitoring Cognitive and Emotional Processes Through Pupil and Cardiac Response During Dynamic Versus Logical Task

The paper deals with the links between physiological measurements and cognitive and emotional functioning. As long as the operator is a key agent in charge of complex systems, the definition of metrics able to predict his performance is a great challenge. The measurement of the physiological state is a very promising way but a very acute comprehension is required; in particular few studies compare autonomous nervous system reactivity according to specific cognitive processes during task performance and task related psychological stress is often ignored. We compared physiological parameters recorded on 24 healthy subjects facing two neuropsychological tasks: a dynamic task that require problem solving in a world that continually evolves over time and a logical task representative of cognitive processes performed by operators facing everyday problem solving. Results showed that the mean pupil diameter change was higher during the dynamic task; conversely, the heart rate was more elevated during the logical task. Finally, the systolic blood pressure seemed to be strongly sensitive to psychological stress. A better taking into account of the precise influence of a given cognitive activity and both workload and related task-induced psychological stress during task performance is a promising way to better monitor operators in complex working situations to detect mental overload or pejorative stress factor of error.     

The Curvilinear Relationship between State Neuroticism and Momentary Task Performance

A daily diary and two experience sampling studies were carried out to investigate curvilinearity of the within-person relationship between state neuroticism and task performance, as well as the moderating effects of within-person variation in momentary job demands (i.e., work pressure and task complexity). In one, results showed that under high work pressure, the state neuroticism–task performance relationship was best described by an exponentially decreasing curve, whereas an inverted U-shaped curve was found for tasks low in work pressure, while in another study, a similar trend was visible for task complexity. In the final study, the state neuroticism–momentary task performance relationship was a linear one, and this relationship was moderated by momentary task complexity. Together, results from all three studies showed that it is important to take into account the moderating effects of momentary job demands because within-person variation in job demands affects the way in which state neuroticism relates to momentary levels of task performance. Specifically, we found that experiencing low levels of state neuroticism may be most beneficial in high demanding tasks, whereas more moderate levels of state neuroticism are optimal under low momentary job demands.     

Classifying Response Correctness across Different Task Sets: A Machine Learning Approach

Erroneous behavior usually elicits a distinct pattern in neural waveforms. In particular, inspection of the concurrent recorded electroencephalograms (EEG) typically reveals a negative potential at fronto-central electrodes shortly following a response error (Ne or ERN) as well as an error-awareness-related positivity (Pe). Seemingly, the brain signal contains information about the occurrence of an error. Assuming a general error evaluation system, the question arises whether this information can be utilized in order to classify behavioral performance within or even across different cognitive tasks. In the present study, a machine learning approach was employed to investigate the outlined issue. Ne as well as Pe were extracted from the single-trial EEG signals of participants conducting a flanker and a mental rotation task and subjected to a machine learning classification scheme (via a support vector machine, SVM). Overall, individual performance in the flanker task was classified more accurately, with accuracy rates of above 85%. Most importantly, it was even feasible to classify responses across both tasks. In particular, an SVM trained on the flanker task could identify erroneous behavior with almost 70% accuracy in the EEG data recorded during the rotation task, and vice versa. Summed up, we replicate that the response-related EEG signal can be used to identify erroneous behavior within a particular task. Going beyond this, it was possible to classify response types across functionally different tasks. Therefore, the outlined methodological approach appears promising with respect to future applications.     

Strain differences in activity and emotionality do not account for differences in learning and memory performance between C57BL/6 and DBA/2 mice

This study examined emotionality, activity, learning and memory, as well as the influence of emotionality and activity on learning and memory performance in C57BL/6 and DBA/2 mice using a mouse-test battery. DBA/2 mice performed more poorly than C57BL/6 mice in complex learning tasks such as the water maze and object recognition tasks. In contrast, C57BL/6 mice showed attenuated habituation to novelty in the open field apparatus and poorer performance in the step-down passive avoidance task. The C57BL/6 mice were less exploratory and more anxious than the DBA/2 mice. The anxiety score (open arm entries in the elevated plus maze) was significantly correlated with all measures of learning and memory in the object recognition task, and some measures in the passive avoidance and water maze tasks. Analysis of covariance (with open arm entries as a covariate) revealed that some measures on trial 1 of the object recognition task, but not the memory scores on trial 2, were confounded by anxiety. No confounding factors of anxiety were found in the water maze or passive avoidance tasks. Similar results were obtained with the activity scores (line crossing and rearing in the open field). In conclusion, strain differences in activity and anxiety did not account for strain differences in learning and memory performance of C57BL/6 and DBA/2 mice. Nonetheless, the importance of using complete behavioural test batteries should be stressed to ensure that strain differences in learning and memory tasks are not confounded by non-cognitive factors.     

Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods

MOTIVATION: A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, the elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from microarray data. In this study, we applied fuzzy c-means (FCM) and normal mixture modeling (NMM) based classification methods to separate microarray data into reliable and unreliable signal intensity populations. RESULTS: We compared the results of FCM classification with those of classification based on NMM. Both approaches were validated against reference sets of biological data consisting of only true positives and true negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. Although a comparison of the computation times indicated that the fuzzy approach is computationally more efficient, other considerations support the use of NMM for the reliability analysis of microarray data. AVAILABILITY: The classification approaches described in this paper and sample microarray data are available as Matlab( TM ) (The MathWorks Inc., Natick, MA) programs (mfiles) and text files, respectively, at http://rc.kfshrc.edu.sa/bssc/staff/MusaAsyali/Downloads.asp. The programs can be run/tested on many different computer platforms where Matlab is available. CONTACT: asyali@kfshrc.edu.sa.     

Unexpected Dual Task Benefits on Cycling in Parkinson Disease and Healthy Adults: A Neuro-Behavioral Model

BackgroundWhen performing two tasks at once, a dual task, performance on one or both tasks typically suffers. People with Parkinson’s disease (PD) usually experience larger dual task decrements on motor tasks than healthy older adults (HOA). Our objective was to investigate the decrements in cycling caused by performing cognitive tasks with a range of difficulty in people with PD and HOAs.MethodsTwenty-eight participants with Parkinson’s disease and 20 healthy older adults completed a baseline cycling task with no secondary tasks and then completed dual task cycling while performing 12 tasks from six cognitive domains representing a wide range of difficulty.ResultsCycling was faster during dual task conditions than at baseline, and was significantly faster for six tasks (all p<.02) across both groups. Cycling speed improved the most during the easiest cognitive tasks, and cognitive performance was largely unaffected. Cycling improvement was predicted by task difficulty (p<.001). People with Parkinson’s disease cycled slower (p<.03) and showed reduced dual task benefits (p<.01) than healthy older adults.ConclusionsUnexpectedly, participants’ motor performance improved during cognitive dual tasks, which cannot be explained in current models of dual task performance. To account for these findings, we propose a model integrating dual task and acute exercise approaches which posits that cognitive arousal during dual tasks increases resources to facilitate motor and cognitive performance, which is subsequently modulated by motor and cognitive task difficulty. This model can explain both the improvement observed on dual tasks in the current study and more typical dual task findings in other studies.     

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm (the visual oddball task) with a long history of behavioral and electroencephalography (EEG) experiments to a functional magnetic resonance imaging (fMRI) experiment is considered. The aims of this paper are to briefly describe fMRI and when its use is appropriate in cognitive neuroscience; illustrate how task design can influence the results of an fMRI experiment, particularly when that task is borrowed from another imaging modality; explain the practical aspects of performing an fMRI experiment. It is demonstrated that manipulating the task demands in the visual oddball task results in different patterns of blood oxygen level dependent (BOLD) activation. The nature of the fMRI BOLD measure means that many brain regions are found to be active in a particular task. Determining the functions of these areas of activation is very much dependent on task design and analysis. The complex nature of many fMRI tasks means that the details of the task and its requirements need careful consideration when interpreting data. The data show that this is particularly important in those tasks relying on a motor response as well as cognitive elements and that covert and overt responses should be considered where possible. Furthermore, the data show that transferring an EEG paradigm to an fMRI experiment needs careful consideration and it cannot be assumed that the same paradigm will work equally well across imaging modalities. It is therefore recommended that the design of an fMRI study is pilot tested behaviorally to establish the effects of interest and then pilot tested in the fMRI environment to ensure appropriate design, implementation and analysis for the effects of interest.     

Machine Learning for Biomedical Literature Triage

This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). The results show that the most fitting model to handle the imbalanced datasets of the triage classification task is obtained by using domain relevant features, an under-sampling technique, and the Logistic Model Trees algorithm.