EEG workload estimation and classification: a systematic review.

OBJECTIVE: Electroencephalography (EEG) has evolved into an indispensable instrument for estimating cognitive workload in various domains. ML and DL techniques have been increasingly employed to develop accurate workload estimation and classification models based on EEG data. The goal of this systematic review is to compile the body of research on EEG workload estimation and classification using ML and DL approaches. METHODS: The PRISMA procedures were followed in conducting the review, searches were conducted through databases at SpringerLink, ACM Digital Library, IEEE Explore, PUBMED, and Science Direct from the beginning to the end of February 16, 2024. Studies were selected based on predefined inclusion criteria. Data were extracted to capture study design, participant demographics, EEG features, ML/DL algorithms, and reported performance metrics. RESULTS: Out of the 125 items that emerged, 33 scientific papers were fully evaluated. The study designs, participant demographics, and EEG workload measurement and categorization techniques used in the investigations differed. SVM, CNN, and hybrid networks are examples of ML and DL approaches that were often used. Analyzing the accuracy scores achieved by different ML/DL models. Furthermore, a relationship was noted between sample frequency and model accuracy, with higher sample frequencies generally leading to improved performance. The percentage distribution of ML/DL methods revealed that SVMs, CNNs, and RNNs were the most commonly utilized techniques, reflecting their robustness in handling EEG data. SIGNIFICANCE: The comprehensive review emphasizes how ML may be used to identify mental workload across a variety of disciplines using EEG data. Optimizing practical applications requires multimodal data integration, standardization efforts, and real-world validation studies. These systems will also be further improved by addressing ethical issues and investigating new EEG properties, which will improve human-computer interaction and performance assessment.

Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG.

The aptitude-oriented exercises from almost all domains impose cognitive load on their operators. Evaluating such load poses several challenges owing to many factors like measurement mode and complexity, nature of the load, overloading conditions, etc. Nevertheless, the physiological measurement of a specific genre of cognitive load and subjective measurement have not been reported along with each other. In this study, the electroencephalography (EEG)-driven machine learning (Support Vector Machine (SVM)) model is sought along with the support of NASA's Task Load Index (NASA-TLX) rating scale for a novel purpose in workload exploration of operators. The Cognitive Load Theory (CLT) was used as the foundation to design the intrinsic stimulus (Spot the Difference task), as most workloads operators are exposed to are notably intrinsic. The SVM-based three-level classification accuracy ranged from 85.4 to 97.4% (p < 0.05), and the NASA-TLX-based three-level classification accuracy ranged from 88.33 to 97.33%. The t-test results show that the neurometric indices contributing to the classification significantly differed (p < 0.05) for every level. The NASA-TLX scale was utilised for validation in its basic form after the validity (Pearson correlation coefficients 0.338 to 0.805 (p < 0.05)) and reliability (Cronbach's α = 0.753) test. This modeling is beneficial to phase out particular-level cognitive exercises from the curriculum during under or overload workload (critical) conditions.

Cognitive tasks and combined statistical methods to evaluate, model, and predict mental workload.

Mental workload (MWL) is a concept that is used as a reference for assessing the mental cost of activities. In recent times, challenges related to user experience are determining the expected MWL value for a given activity and real-time adaptation of task complexity level to achieve or maintain desired MWL. As a consequence, it is important to have at least one task that can reliably predict the MWL level associated with a given complexity level. In this study, we used several cognitive tasks to meet this need, including the N-Back task, the commonly used reference test in the MWL literature, and the Corsi test. Tasks were adapted to generate different MWL classes measured via NASA-TLX and Workload Profile questionnaires. Our first objective was to identify which tasks had the most distinct MWL classes based on combined statistical methods. Our results indicated that the Corsi test satisfied our first objective, obtaining three distinct MWL classes associated with three complexity levels offering therefore a reliable model (about 80% accuracy) to predicted MWL classes. Our second objective was to achieve or maintain the desired MWL, which entailed the use of an algorithm to adapt the MWL class based on an accurate prediction model. This model needed to be based on an objective and real-time indicator of MWL. For this purpose, we identified different performance criteria for each task. The classification models obtained indicated that only the Corsi test would be a good candidate for this aim (more than 50% accuracy compared to a chance level of 33%) but performances were not sufficient to consider identifying and adapting the MWL class online with sufficient accuracy during a task. Thus, performance indicators require to be complemented by other types of measures like physiological ones. Our study also highlights the limitations of the N-back task in favor of the Corsi test which turned out to be the best candidate to model and predict the MWL among several cognitive tasks.

Cognitive Analyses for Interface Design Using Dual N-Back Tasks for Mental Workload (MWL) Evaluation.

In the manufacturing environments of today, human-machine systems are constituted with complex and advanced technology, which demands workers' considerable mental workload. This work aims to design and evaluate a Graphical User Interface developed to induce mental workload based on Dual N-Back tasks for further analysis of human performance. This study's contribution lies in developing proper cognitive analyses of the graphical user interface, identifying human error when the Dual N-Back tasks are presented in an interface, and seeking better user-system interaction. Hierarchical task analysis and the Task Analysis Method for Error Identification were used for the cognitive analysis. Ten subjects participated voluntarily in the study, answering the NASA-TLX questionnaire at the end of the task. The NASA-TLX results determined the subjective participants' mental workload proving that the subjects were induced to different levels of mental workload (Low, Medium, and High) based on the ANOVA statistical results using the mean scores obtained and cognitive analysis identified redesign opportunities for graphical user interface improvement.

Towards a versatile mental workload modeling using neurometric indices.

Researchers have been working to magnify mental workload (MWL) modeling for a long time. An important aspect of its modeling is feature selection as it interprets bulky and high-dimensional EEG data and enhances the accuracy of the classification model. In this study, a feature selection technique is proposed to obtain an optimized feature set with multiple domain features that can contribute to classifying the MWL at three distinct levels. The brain signals from thirteen healthy subjects were examined while they attended an intrinsic MWL of spotting differences in a set of similar pictures. The Recursive Feature Elimination (RFE) technique selects the robust features from the feature matrix by eliminating all the least contributing features. Along with the Support Vector Machine (SVM), the overall classification accuracy with the proposed RFE reached 0.913 from 0.791 surpassing the other techniques mentioned. The results of the study also significantly display the variation in the mean values of the selected features at the three workload levels (p<0.05). This model can become the principle for defining the workload level quantification applicable to diverse fields like neuroergonomics study, intelligent assistive devices (ADs) development, blue-chip technology exploration, cognitive evaluation of students, power plant operators, traffic operators, etc.

Fast pyrolysis of guaiacyl-syringyl (GS) type milled wood lignin: Product characteristics and CH4 formation mechanism study.

It is anticipated that the insight into the demethylation and mechanism of CH4 formation from natural lignin using in-situ diffuse reflectance infrared Fourier transform spectroscopy (in-situ FTIR) combined with two-dimensional perturbation correlation infrared spectroscopy (2D-PCIS) and density functional theory (DFT) calculation analysis would contribute to a deeper insight of bond cleavage mechanism of lignin pyrolysis. Herein, GS-type lignin (poplar MWL) was characterized by Fourier transform infrared spectroscopy (FTIR) and heteronuclear Single-Quantum Correlation Nuclear Magnetic Resonance (HSQC), and its pyrolysis at different temperatures was performed in a lab-scale fixed-bed reactor. The biochar, gaseous and liquid products were qualitative, and quantitative analysis of gases and bio-oil is demonstrated using gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). The key of CH4 formation is the homolytic cleavage of the methoxyl functional group generating methyl radical and further verified via in-situ FTIR combined with 2D-PCIS and DFT calculation. The study established a new methodology based on multiple factor analysis to evaluate the CH4 formation mechanism in GS-type milled wood lignin at the molecular level, which is of positive significance for increasing lignin valorization and improving the environment.

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain-Machine Interface (BMI).

Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain-machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier-support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.

Structural Changes in Milled Wood Lignin (MWL) of Chinese Quince (Chaenomeles sinensis) Fruit Subjected to Subcritical Water Treatment.

Subcritical water treatment has received considerable attention due to its cost effectiveness and environmentally friendly properties. In this investigation, Chinese quince fruits were submitted to subcritical water treatment (130, 150, and 170 °C), and the influence of treatments on the structure of milled wood lignin (MWL) was evaluated. Structural properties of these lignin samples (UL, L130, L150, and L170) were investigated by high-performance anion exchange chromatography (HPAEC), FT-IR, gel permeation chromatography (GPC), TGA, pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), 2D-Heteronculear Single Quantum Coherence (HSQC) -NMR, and 31P-NMR. The carbohydrate analysis showed that xylose in the samples increased significantly with higher temperature, and according to molecular weight and thermal analysis, the MWLs of the pretreated residues have higher thermal stability with increased molecular weight. The spectra of 2D-NMR and 31P-NMR demonstrated that the chemical linkages in the MWLs were mainly ß-O-4' ether bonds, ß-5' and ß-ß', and the units were principally G- S- H- type with small amounts of ferulic acids; these results are consistent with the results of Py-GC/MS analysis. It is believed that understanding the structural changes in MWL caused by subcritical water treatment will contribute to understanding the mechanism of subcritical water extraction, which in turn will provide a theoretical basis for developing the technology of subcritical water extraction.

Utilizing the lateral excess for autologous augmentation in massive weight loss patients.

The number of patients in need of a mastopexy with autologous augmentation after massive weight loss (MWL) increases along with the increasing number of bariatric surgeries. The primary aim of this paper was to visualize how we utilize the lateral excess of the breast for auto augmentation. The secondary aim was to assess the outcome using the LOPOSAM technique in a larger study population. This retrospective study included 72 MWL patients aged 40±9 years undergoing a bilateral LOPOSAM procedure, between March 2015 and April 2018. All patients had undergone a weight loss of more than 15 BMI units, had a BMI of less than 30 kg/m2 at the time of surgery and functional problems due to excess or lax skin. Patient demographics, comorbidities, cause of MWL, operative time and complications were recorded. The mean weight loss was 58±18 kg or 21±6 BMI units. The mean operative time was 97±39 minutes. The surgical goal was achieved in all patients. Three patients (4%) experienced hematomas requiring surgical intervention. The utilization of the lateral excess of the breast for auto augmentation in MWL patients is visualized. The long term results using the LOPOSAM mastopexy technique shows that the technique is quick, safe and with a low rate of complications.

Acetyl Groups in Typha capensis: Fate of Acetates during Organosolv and Ionosolv Pulping.

During biomass fractionation, any native acetylation of lignin and heteropolysaccharide may affect the process and the resulting lignin structure. In this study, Typha capensis (TC) and its lignin isolated by milling (MWL), ionosolv (ILL) and organosolv (EOL) methods were investigated for acetyl group content using FT-Raman, ¹H NMR, 2D-NMR, back-titration, and Zemplén transesterification analytical methods. The study revealed that TC is a highly acetylated grass; extractive free TC (TCextr) and TC MWL exhibited similar values of acetyl content: 6 wt % and 8 wt % by Zemplén transesterification, respectively, and 11 wt % by back-titration. In contrast, lignin extracted from organosolv and [EMIm][OAc] pulping lost 80% of the original acetyl groups. With a high acetyl content in the natural state, TC could be an interesting raw material in biorefinery in which acetic acid could become an important by-product.

Orders- Versus Encounters-Based Image Capture: Implications Pre- and Post-Procedure Workflow, Technical and Build Capabilities, Resulting, Analytics and Revenue Capture: HIMSS-SIIM Collaborative White Paper.

The decision to implement an orders-based versus an encounters-based imaging workflow poses various implications to image capture and storage. The impacts include workflows before and after an imaging procedure, electronic health record build, technical infrastructure, analytics, resulting, and revenue. Orders-based workflows tend to favor some imaging specialties while others require an encounters-based approach. The intent of this HIMSS-SIIM white paper is to offer lessons learned from early adopting institutions to physician champions and informatics leadership developing strategic planning and operational rollouts for specialties capturing clinical multimedia.

Test-Retest Reliability of Word Recognition Score Using Korean Standard Monosyllabic Word Lists for Adults as a Function of the Number of Test Words.

BACKGROUND AND OBJECTIVES: The purpose was to establish the test-retest reliability of word recognition score (WRS) using Korean standard monosyllabic word lists for adults (KS-MWL-A) recently developed based on the international standard for speech audiometry (ISO 8253-3:2012). SUBJECTS AND METHODS: Subjects consisted of 159 adults aged to 18 to 25 years with normal hearing sensitivity. WRSs were obtained in 2 dB steps from the level of speech recognition thresholds to the level of 86% correct responses or greater. After one or two weeks, retest was performed. Correlation, confidence interval (CI) and prediction interval (PI) were calculated for the reliability. RESULTS: Correlation coefficients were 0.88 for 50 test words, 0.76 for 25 and 0.61 for 10 words. Results also showed that 95% CIs and PIs were narrower for 25 and 50 test words than those for 10 test words. CONCLUSIONS: Korean WRS using the KS-MWL-A has high reliability for 25 and 50 test words, but relatively low for 10 words. It suggested that 95% CIs for each test words would be criteria for significant differences in WRS for groups and 95% PIs at each score of WRS could be utilized for a considerable difference for each individual at retest.

Test-Retest Reliability of Word Recognition Score Using Korean Standard Monosyllabic Word Lists for Adults as a Function of the Number of Test Words

BACKGROUND AND OBJECTIVES: The purpose was to establish the test-retest reliability of word recognition score (WRS) using Korean standard monosyllabic word lists for adults (KS-MWL-A) recently developed based on the international standard for speech audiometry (ISO 8253-3:2012). SUBJECTS AND METHODS: Subjects consisted of 159 adults aged to 18 to 25 years with normal hearing sensitivity. WRSs were obtained in 2 dB steps from the level of speech recognition thresholds to the level of 86% correct responses or greater. After one or two weeks, retest was performed. Correlation, confidence interval (CI) and prediction interval (PI) were calculated for the reliability. RESULTS: Correlation coefficients were 0.88 for 50 test words, 0.76 for 25 and 0.61 for 10 words. Results also showed that 95% CIs and PIs were narrower for 25 and 50 test words than those for 10 test words. CONCLUSIONS: Korean WRS using the KS-MWL-A has high reliability for 25 and 50 test words, but relatively low for 10 words. It suggested that 95% CIs for each test words would be criteria for significant differences in WRS for groups and 95% PIs at each score of WRS could be utilized for a considerable difference for each individual at retest.

Recent developments and applications of immobilized laccase.

Laccase is a promising biocatalyst with many possible applications, including bioremediation, chemical synthesis, biobleaching of paper pulp, biosensing, textile finishing and wine stabilization. The immobilization of enzymes offers several improvements for enzyme applications because the storage and operational stabilities are frequently enhanced. Moreover, the reusability of immobilized enzymes represents a great advantage compared with free enzymes. In this work, we discuss the different methodologies of enzyme immobilization that have been reported for laccases, such as adsorption, entrapment, encapsulation, covalent binding and self-immobilization. The applications of laccase immobilized by the aforementioned methodologies are presented, paying special attention to recent approaches regarding environmental applications and electrobiochemistry.