Task-Independent Cognitive Workload Discrimination Based on EEG with Stacked Graph Attention Convolutional Networks.

We aimed to provide an assessment of the impact of workload manipulations on various cardiac measurements. We further sought to determine the most effective measurement approaches of cognitive workload as well as quantify the conditions under which these measures are most effective for interpretation. Cognitive workload affects human performance, particularly when load is relatively high (overload) or low (underload). Despite ongoing interest in assessing cognitive workload through cardiac measures, it is currently unclear which cardiac-based assessments best indicate cognitive workload. Although several quantitative studies and qualitative reviews have sought to provide guidance, no meta-analytic integration of cardiac assessment(s) of cognitive workload exists to date. We used Morris and DeShon's meta-analytic procedures to quantify the changes in cardiac measures due to task load conditions. Sample-weighted Cohen's d values suggest that several metrics of cardiac activity demonstrate sensitivity in response to cognitive workload manipulations. Heart rate variability measures show sensitivity to task load, conditions of event rate, and task duration. Authors of future work should seek to quantify the utility of leveraging multiple metrics to understand workload. Results suggest that assessment of cognitive workload can be done using various cardiac activity indicators. Further, given the number of valid and reliable measures available, researchers and practitioners should base their selection of a psychophysiological measure on the experimental and practical concerns inherent to their task/protocol. Findings bear implications for future assessment of cognitive workload within basic and applied settings. Future research should seek to validate conditions under which measurements are best interpreted, including but not limited to individual differences.

Bipolar disorder (BD) is a common psychiatric disorder, but its pathophysiology is not fully elucidated. The current study focused on its electrophysiological characteristics, especially power spectral density (PSD). Resting state with eyes opened magnetoencephalography data were collected from 21 patients with BD and 22 healthy controls. The whole brain's PSD was calculated from source reconstructed waveforms at each frequency band (delta: 1-3 Hz, theta: 4-7 Hz, alpha: 8-12 Hz, low beta: 13-19 Hz, high beta: 20-29 Hz, and gamma: 30-80 Hz). We compared PSD values on the marked vertices at each frequency band between healthy and patient groups using a Mann-Whitney rank test to examine the relationship between significantly different PSD and clinical measures. The PSD in patients with BD was significantly decreased in lower frequency bands, mainly in the default mode network (DMN) areas (bilateral medial prefrontal cortex, bilateral precuneus, left inferior parietal lobe, and right temporal cortex in the alpha band) and salience network areas (SAL; left anterior insula [AI] at the delta band, anterior cingulate cortex at the theta band, and right AI at the alpha band). No significant differences in PSD were observed at low beta and high beta. PSD was not correlated with age or other clinical scales. Altered PSDs of the DMN and SAL were observed in the delta, theta, and alpha bands. These alterations contribute to the vulnerability of BD through the disturbance of self-referential mental activity and switching between the default mode and frontoparietal networks.

Objective: Cross-subject generalization in EEG-based seizure detection remains a significant challenge due to diverse physiological differences among patients, which can degrade model performance across individuals. This work aims to develop a robust, lightweight, and fast-deployable model for cross-subject seizure detection that achieves high accuracy while enabling real-time, low-latency inference on resource-constrained devices. Methods: We propose PSD-LW-DCN, a novel lightweight deep convolutional network that leverages power spectral density (PSD) features extracted via multitaper spectral estimation. Raw EEG signals are preprocessed through bandpass filtering, segmentation, and class balancing. PSD features are computed across multiple frequency bands ($$\delta$$,$$\theta$$,$$\alpha$$,$$\beta$$,$$\gamma$$), averaged across channels to enhance generalizability, and concatenated as input to the network. The model was trained and evaluated separately on the CHB-MIT and TUSZ datasets, with no data mixing between them. Results: With only 61,218 parameters, PSD-LW-DCN achieves state-of-the-art efficiency, reducing inference time to 1.9 ms/sample on CHB-MIT and 2.1 ms/sample on TUSZ, demonstrating its suitability for real-time applications. It attains 85.84% accuracy on CHB-MIT and 83.21% on TUSZ, representing improvements of 5% and 3%, respectively, over previous approaches. Notably, the model shows strong cross-subject generalization and robustness across diverse patient populations. In clinically meaningful event-based evaluation, it achieves a low false alarm rate of 0.33 false alarms per hour (FA/h) on CHB-MIT and 1.09 FA/h on TUSZ, indicating high operational reliability in long-term monitoring scenarios. Conclusions: PSD-LW-DCN offers an optimal balance between accuracy, speed, and model size, making it highly suitable for real-time, edge-based clinical diagnosis of epilepsy. Its efficiency and low false alarm burden confirm its potential for deployment in low-power wearable and point-of-care systems. Future work will focus on further reducing the false alarm rate, particularly in more heterogeneous datasets like TUSZ, to enhance clinical usability.

Cognitive workload assessment plays an important role to examine the mental status of brain for emergency situations faced by air traffic controllers, military personals and many more. It is also significant for brain disorder diagnosis and psychological health monitoring. Mental workload induced by various factors needs to be critically examined for better decisions. This paper presents an automated model for cognitive workload assessment to provide accurate categorization on intensity of workload induced by multitasking situations. Statistical wavelets corresponding to brain frequencies act as features for categorization task. The brain analysis needs technical expertise to identify the relevant information but, due to lack of sufficient number of experts, feature selection model helps to identify the relevant features responsible to categorize cognitive workload. Teaching Learning based Optimization (TLBO) method has been modified using `Logical Operators' for binary feature selection problem in the current study and evaluated based on different parameters. Results on Logical TLBO reached upto 100% for binary and multiclass classification of cognitive workload. The results also show the proposed method is converging in minimum iteration for multiclass cognitive workload. PROMETHEE based multi-criteria decision-making approach has adopted for ranking of algorithm to select the best algorithm when multiple alternatives are available. Based on study of current literature, this approach appears to be first time implemented to provide ranking of optimization algorithms. The PROMETHEE method also suggests that the Logical TLBO is best among all the other compared approaches.

High-fidelity clinical simulations can be used by clinicians to acquire technical (physical ability and knowledge) and non-technical (cognitive and social processes) skills. Excessive cognitive workload contributes to medical errors because of the impact on both technical and non-technical skills. Many studies measure cognitive workload with psychometric instruments that limit the assessment of cognitive workload to a single time period and may involve response bias. Using eye tracking to measure task-evoked pupillary responses allows the measurement of changes in pupil diameter related to the cognitive workload associated with a specific activity. Incorporating eye tracking with high-fidelity clinical simulations provides a reliable and continuous assessment of cognitive workload. The purpose of this literature review is to summarize the use of eye-tracking technology to measure cognitive workload of healthcare providers to generate evidence-based guidelines for measuring cognitive workload during high-fidelity clinical simulations. What this manuscript adds to the body of literature is a summary of best practices related to the different methods of measuring cognitive workload, benefits and limitations of using eye tracking, and high-fidelity clinical simulation design considerations for successful integration of eye tracking.

EEG-based Neurophysiological Indicators in Pronoun Resolution Using Feature Analysis.

A good night's sleep is essential for human mental and physical health, and chronic sleep deprivation has been related to impaired neurobehavioral function. This study aims to determine the relationship between internet addiction, loneliness, and cognitive workload on sleep quality among public university students in Klang Valley. A total of 400 respondents involved from three public universities in Klang valley. A self-administered questionnaire was used as a tool for data collection. The result revealed that most of the respondents had moderate level of internet addiction, loneliness, and cognitive workload. Meanwhile, the result also showed that majority of the respondents scored low level severity of sleep quality. In addition, cognitive workload, loneliness and internet addiction were positively correlated with sleep quality issues. Multiple regression analysis showed that loneliness and cognitive workload were significant predictors of sleep quality issues among the students with cognitive workload as the strongest predictor. This study concluded that undergraduate students’ cognitive workload, loneliness and internet addiction were important in influencing the undergraduate students’ sleep quality issues. By understanding the impact of these psychological factors on sleep quality particularly among the undergraduate students, it helped to minimize the possibility of undergraduate students' sleep quality issues in order to promote and maintain a healthy lifestyle for them. Thus, the impact and implications of this study will be discussed.

Gear fault diagnosis generally uses oscillation signals to extract fault features, but the oscillation identify technique is inconvenient to set up sensors and is easily affected by the surrounding and noise. The motor itself has the property of a sensor, and signals such as motor current is able to reflect the change of the load torque. This paper proposes a gear fault detection approach, which applies the Motor Current Signature Analysis (MCSA) to exploit spectral characteristics to recognize malfunction in servo motor drive gears. First, we calculate the nominal revolutions per minute (rpm) to explore frequencies of interest. Second, frequency bands where faulty signals would be appear are constructed. Next, we extract power spectral density (PSD) feature data. Finally, the paper computes spectral metrics for the frequency band of interest. By using two spectral metrics, gear health and failure data are clearly grouped in different areas of the scatter chart. The experimental results give evidence of the effectiveness of analyzing current characteristics of servo motors to classify fault and health data.

In the study of human cognitive activity using electroencephalogram (EEG), the brain dynamics parameters and characteristics play a crucial role. They allow to investigate the changes in functionality depending on the environment and task performance process, and also to access the intensity of the brain activity in various locations of the cortex and its dependencies. Usually, the dynamics of activation of different brain areas during the cognitive tasks are being studied by spectral analysis based on power spectral density (PSD) estimation, and coherence analysis, which are de facto standard tools in quantitative characterization of brain activity. PSD and coherence reflect the strength of oscillations and similarity of the emergence of these oscillations in the brain, respectively, while the concept of stability of brain activity over time is not well defined and less formalized. We propose to employ the detrended fluctuation analysis (DFA) as a measure of the EEG persistence over time, and use the DFA scaling exponent as its quantitative characteristics. We applied DFA to the study of the changes in activation in brain dynamics during mental calculations and united it with PSD and coherence estimation. In the experiment, EEGs during resting state and mental serial subtraction from 36 subjects were recorded and analyzed in four frequency ranges: θ1 (4.1–5.8 Hz), θ2 (5.9–7.4 Hz), β1 (13–19.9 Hz), and β2 (20–25 Hz). PSD maps to access the intensity of cortex activation and coherence to quantify the connections between different brain areas were calculated, the distribution of DFA scaling exponent over the head surface was exploited to measure the time characteristics of the dynamics of brain activity. Obtained arrangements of DFA scaling exponent suggest that normal functioning of the brain is characterized by long-term temporal correlations in the cortex. Topographical distribution of the DFA scaling exponent was comparable for θ and β frequency bands, demonstrating the largest values of DFA scaling exponent during cognitive activation. The study shows that the long-term temporal correlations evaluated by DFA can be of great interest for diagnosis of the variety of brain dysfunctions of different etiology in the future.

Introduction Professors' mental workload is critical as an influential factor in the quality of education and the delivery of educational services. Self-esteem can significantly impact their behavior and performance. This study aimed to investigate the relationship between self-esteem and mental workload in professors. Method This cross-sectional study was conducted in 2023 among 86 professors from all four faculties of Jiroft University of Medical Sciences (Medicine, Nursing and Midwifery, Health, and Paramedical Sciences), who were selected through census sampling. The data were collected using a three-part questionnaire consisting of demographic information, the standardized Rosenberg Self-Esteem Questionnaire, and the NASA Mental Workload Questionnaire. The data were analyzed using the Kolmogorov-Smirnov test, T-test, ANOVA, and Pearson correlation coefficient in SPSS v20 software at a significance level of 0.05. Results The majority of professors were aged 30–40 years, 58.1% were men, 72.1% were married, 48.8% held professional doctorate degrees, 53.5% were affiliated with the medical school, and 65.1% had 1–10 years of work experience. Discussion The findings revealed that professors experienced a higher-than-acceptable mental workload, while their self-esteem levels were categorized as high. Although an inverse relationship was observed between mental workload and self-esteem, it was not statistically significant. However, there was a significant relationship between mental workload and variables such as educational degree and faculty affiliation. Conclusion The study's findings suggest that while higher self-esteem may reduce mental workload among professors, no statistically significant relationship exists between these two factors. Nonetheless, mental workload was significantly associated with educational degree and faculty affiliation.

Hyperdimensional (HD) computing is a form of brain-inspired computing which can be applied to numerous classification problems. In past research, it has been shown that seizures can be detected from electroencephalograms (EEG) with high accuracy using local binary pattern (LBP) encoding. This paper explores applicability of binary HD computing to seizure detection from intra-cranial EEG (iEEG) data from the Kaggle seizure detection contest based on using both LBP and power spectral density (PSD) features. In the PSD method, three novel approaches to HD classification are presented for both selected features and all features. These are referred as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single classifier long hypervector</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple classifiers</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single classifier short hypervector</i> . To visualize the quality of classification of test data, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hypervector distance</i> plot is introduced that plots the Hamming distance of the query hpervectors from one class hypervector <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vs.</i> that from the other. Simulation results show that: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1)</i> . LBP method offers an average 80.9% test accuracy, 71.9% sensitivity, 81.4% specificity and 76.6% test AUC whereas the PSD method can achieve an average of 91.0% test accuracy, 81.8% sensitivity, 92.0% specificity and 86.9% test AUC. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2)</i> . The average seizure detection latency is 2.5s for LBP method and is 4.5s for the PSD methods. This average latency, less than 5s, is a relevant parameter for fast drug delivery, indicating that both LBP and PSD methods are able to detect the seizures in a timely manner. The performance using selected PSD features is better than that using all features. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3)</i> . It is shown that the dimensionality of the hypervector can be reduced to 1, 000 bits for LBP and PSD methods from 10, 000. Futhermore, for some approaches of selected features, the dimensionality of the hypervector can be reduced to 100 bits.

Evaluation of cognitive workload finds its application in many areas, from educational program assessment through professional driver health examination to monitoring the mental state of people carrying out jobs of high responsibility, such as pilots or airline traffic dispatchers. Estimation of multilevel cognitive workload is a task usually realized in a subject-dependent way, while the present research is focused on developing the procedure of subject-independent evaluation of cognitive workload level. The aim of the paper is to estimate cognitive workload level in accordance with subject-independent approach, applying classical machine learning methods combined with feature selection techniques. The procedure of data acquisition was based on registering the EEG signal of the person performing arithmetical tasks divided into six intervals of advancement. The analysis included the stages of preprocessing, feature extraction, and selection, while the final step covered multiclass classification performed with several models. The results discussed show high maximal accuracies achieved: ~91% for both the validation dataset and for the cross-validation approach for kNN model.

The integration of brain monitoring into the man-machine interface holds great promise for real-time assessment of operator status and intelligent allocation of tasks between machines and humans. This article presents an integrated hardware and software solution for acquisition and real-time analysis of the electroencephalogram (EEG) to monitor indexes of alertness, cognition, and memory. Three experimental paradigms were evaluated in a total of 45 participants to identify EEG indexes associated with changes in cognitive workload: the Warship Commander Task (WCT), a simulated navy command and control environment that allowed workload levels to be systematically manipulated; a cognitive task with three levels of difficulty and consistent sensory inputs and motor outputs; and a multisession image learning and recognition memory test. Across tasks and participants, specific changes in the EEG were identified that were reliably associated with levels of cognitive workload. The EEG indexes were also shown to change as a function of training on the WCT and the learning and memory task. Future applications of the system to augment cognition in military and industrial environments are discussed.

Due to an abundance of data and dynamic nature of tasks, challenges with information retrieval in surveillance and target identification tasks have risen in today's Intelligence, Surveillance, and Reconnaissance (ISR) community. In this study, two variables, Area of Coverage and Amount of Activity (AOC/ACT), are manipulated to study their effects on the number of Watch Windows an observer can monitor. This research describes the analyst's task model, and explains how the level of AOC/ACT and number of Watch Windows affects the analyst's cognitive load. Results showed a significant difference in performance and physiological indicators of workload between high AOC/ACT conditions and low AOC/ACT conditions. A linear correlation was exhibited between the number of Watch Windows and the number of fixations. The results show that these variables can be manipulated in tasking to maintain appropriate levels of cognitive workload.

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.